Provides APIs for creating and managing Amazon Forecast resources.
', 'operations' => [ 'CreateAutoPredictor' => 'Creates an Amazon Forecast predictor.
Amazon Forecast creates predictors with AutoPredictor, which involves applying the optimal combination of algorithms to each time series in your datasets. You can use CreateAutoPredictor to create new predictors or upgrade/retrain existing predictors.
Creating new predictors
The following parameters are required when creating a new predictor:
PredictorName - A unique name for the predictor.
DatasetGroupArn - The ARN of the dataset group used to train the predictor.
ForecastFrequency - The granularity of your forecasts (hourly, daily, weekly, etc).
ForecastHorizon - The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.
When creating a new predictor, do not specify a value for ReferencePredictorArn.
Upgrading and retraining predictors
The following parameters are required when retraining or upgrading a predictor:
PredictorName - A unique name for the predictor.
ReferencePredictorArn - The ARN of the predictor to retrain or upgrade.
When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName.
Creates an Amazon Forecast dataset. The information about the dataset that you provide helps Forecast understand how to consume the data for model training. This includes the following:
DataFrequency - How frequently your historical time-series data is collected.
Domain and DatasetType - Each dataset has an associated dataset domain and a type within the domain. Amazon Forecast provides a list of predefined domains and types within each domain. For each unique dataset domain and type within the domain, Amazon Forecast requires your data to include a minimum set of predefined fields.
Schema - A schema specifies the fields in the dataset, including the field name and data type.
After creating a dataset, you import your training data into it and add the dataset to a dataset group. You use the dataset group to create a predictor. For more information, see Importing datasets.
To get a list of all your datasets, use the ListDatasets operation.
For example Forecast datasets, see the Amazon Forecast Sample GitHub repository.
The Status of a dataset must be ACTIVE before you can import training data. Use the DescribeDataset operation to get the status.
Creates a dataset group, which holds a collection of related datasets. You can add datasets to the dataset group when you create the dataset group, or later by using the UpdateDatasetGroup operation.
After creating a dataset group and adding datasets, you use the dataset group when you create a predictor. For more information, see Dataset groups.
To get a list of all your datasets groups, use the ListDatasetGroups operation.
The Status of a dataset group must be ACTIVE before you can use the dataset group to create a predictor. To get the status, use the DescribeDatasetGroup operation.
Imports your training data to an Amazon Forecast dataset. You provide the location of your training data in an Amazon Simple Storage Service (Amazon S3) bucket and the Amazon Resource Name (ARN) of the dataset that you want to import the data to.
You must specify a DataSource object that includes an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data, as Amazon Forecast makes a copy of your data and processes it in an internal Amazon Web Services system. For more information, see Set up permissions.
The training data must be in CSV or Parquet format. The delimiter must be a comma (,).
You can specify the path to a specific file, the S3 bucket, or to a folder in the S3 bucket. For the latter two cases, Amazon Forecast imports all files up to the limit of 10,000 files.
Because dataset imports are not aggregated, your most recent dataset import is the one that is used when training a predictor or generating a forecast. Make sure that your most recent dataset import contains all of the data you want to model off of, and not just the new data collected since the previous import.
To get a list of all your dataset import jobs, filtered by specified criteria, use the ListDatasetImportJobs operation.
', 'CreateExplainability' => 'Explainability is only available for Forecasts and Predictors generated from an AutoPredictor (CreateAutoPredictor)
Creates an Amazon Forecast Explainability.
Explainability helps you better understand how the attributes in your datasets impact forecast. Amazon Forecast uses a metric called Impact scores to quantify the relative impact of each attribute and determine whether they increase or decrease forecast values.
To enable Forecast Explainability, your predictor must include at least one of the following: related time series, item metadata, or additional datasets like Holidays and the Weather Index.
CreateExplainability accepts either a Predictor ARN or Forecast ARN. To receive aggregated Impact scores for all time series and time points in your datasets, provide a Predictor ARN. To receive Impact scores for specific time series and time points, provide a Forecast ARN.
CreateExplainability with a Predictor ARN
You can only have one Explainability resource per predictor. If you already enabled ExplainPredictor in CreateAutoPredictor, that predictor already has an Explainability resource.
The following parameters are required when providing a Predictor ARN:
ExplainabilityName - A unique name for the Explainability.
ResourceArn - The Arn of the predictor.
TimePointGranularity - Must be set to “ALL”.
TimeSeriesGranularity - Must be set to “ALL”.
Do not specify a value for the following parameters:
DataSource - Only valid when TimeSeriesGranularity is “SPECIFIC”.
Schema - Only valid when TimeSeriesGranularity is “SPECIFIC”.
StartDateTime - Only valid when TimePointGranularity is “SPECIFIC”.
EndDateTime - Only valid when TimePointGranularity is “SPECIFIC”.
CreateExplainability with a Forecast ARN
You can specify a maximum of 50 time series and 500 time points.
The following parameters are required when providing a Predictor ARN:
ExplainabilityName - A unique name for the Explainability.
ResourceArn - The Arn of the forecast.
TimePointGranularity - Either “ALL” or “SPECIFIC”.
TimeSeriesGranularity - Either “ALL” or “SPECIFIC”.
If you set TimeSeriesGranularity to “SPECIFIC”, you must also provide the following:
DataSource - The S3 location of the CSV file specifying your time series.
Schema - The Schema defines the attributes and attribute types listed in the Data Source.
If you set TimePointGranularity to “SPECIFIC”, you must also provide the following:
StartDateTime - The first timestamp in the range of time points.
EndDateTime - The last timestamp in the range of time points.
Exports an Explainability resource created by the CreateExplainability operation. Exported files are exported to an Amazon Simple Storage Service (Amazon S3) bucket.
You must specify a DataDestination object that includes an Amazon S3 bucket and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles.
The Status of the export job must be ACTIVE before you can access the export in your Amazon S3 bucket. To get the status, use the DescribeExplainabilityExport operation.
Creates a forecast for each item in the TARGET_TIME_SERIES dataset that was used to train the predictor. This is known as inference. To retrieve the forecast for a single item at low latency, use the operation. To export the complete forecast into your Amazon Simple Storage Service (Amazon S3) bucket, use the CreateForecastExportJob operation.
The range of the forecast is determined by the ForecastHorizon value, which you specify in the CreatePredictor request. When you query a forecast, you can request a specific date range within the forecast.
To get a list of all your forecasts, use the ListForecasts operation.
The forecasts generated by Amazon Forecast are in the same time zone as the dataset that was used to create the predictor.
For more information, see howitworks-forecast.
The Status of the forecast must be ACTIVE before you can query or export the forecast. Use the DescribeForecast operation to get the status.
By default, a forecast includes predictions for every item (item_id) in the dataset group that was used to train the predictor. However, you can use the TimeSeriesSelector object to generate a forecast on a subset of time series. Forecast creation is skipped for any time series that you specify that are not in the input dataset. The forecast export file will not contain these time series or their forecasted values.
Exports a forecast created by the CreateForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket. The forecast file name will match the following conventions:
<ForecastExportJobName>_<ExportTimestamp>_<PartNumber>
where the <ExportTimestamp> component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ).
You must specify a DataDestination object that includes an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles.
For more information, see howitworks-forecast.
To get a list of all your forecast export jobs, use the ListForecastExportJobs operation.
The Status of the forecast export job must be ACTIVE before you can access the forecast in your Amazon S3 bucket. To get the status, use the DescribeForecastExportJob operation.
Creates a predictor monitor resource for an existing auto predictor. Predictor monitoring allows you to see how your predictor\'s performance changes over time. For more information, see Predictor Monitoring.
', 'CreatePredictor' => 'This operation creates a legacy predictor that does not include all the predictor functionalities provided by Amazon Forecast. To create a predictor that is compatible with all aspects of Forecast, use CreateAutoPredictor.
Creates an Amazon Forecast predictor.
In the request, provide a dataset group and either specify an algorithm or let Amazon Forecast choose an algorithm for you using AutoML. If you specify an algorithm, you also can override algorithm-specific hyperparameters.
Amazon Forecast uses the algorithm to train a predictor using the latest version of the datasets in the specified dataset group. You can then generate a forecast using the CreateForecast operation.
To see the evaluation metrics, use the GetAccuracyMetrics operation.
You can specify a featurization configuration to fill and aggregate the data fields in the TARGET_TIME_SERIES dataset to improve model training. For more information, see FeaturizationConfig.
For RELATED_TIME_SERIES datasets, CreatePredictor verifies that the DataFrequency specified when the dataset was created matches the ForecastFrequency. TARGET_TIME_SERIES datasets don\'t have this restriction. Amazon Forecast also verifies the delimiter and timestamp format. For more information, see howitworks-datasets-groups.
By default, predictors are trained and evaluated at the 0.1 (P10), 0.5 (P50), and 0.9 (P90) quantiles. You can choose custom forecast types to train and evaluate your predictor by setting the ForecastTypes.
AutoML
If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the objective function, set PerformAutoML to true. The objective function is defined as the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses. For more information, see EvaluationResult.
When AutoML is enabled, the following properties are disallowed:
AlgorithmArn
HPOConfig
PerformHPO
TrainingParameters
To get a list of all of your predictors, use the ListPredictors operation.
Before you can use the predictor to create a forecast, the Status of the predictor must be ACTIVE, signifying that training has completed. To get the status, use the DescribePredictor operation.
Exports backtest forecasts and accuracy metrics generated by the CreateAutoPredictor or CreatePredictor operations. Two folders containing CSV or Parquet files are exported to your specified S3 bucket.
The export file names will match the following conventions:
<ExportJobName>_<ExportTimestamp>_<PartNumber>.csv
The <ExportTimestamp> component is in Java SimpleDate format (yyyy-MM-ddTHH-mm-ssZ).
You must specify a DataDestination object that includes an Amazon S3 bucket and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles.
The Status of the export job must be ACTIVE before you can access the export in your Amazon S3 bucket. To get the status, use the DescribePredictorBacktestExportJob operation.
What-if analysis is a scenario modeling technique where you make a hypothetical change to a time series and compare the forecasts generated by these changes against the baseline, unchanged time series. It is important to remember that the purpose of a what-if analysis is to understand how a forecast can change given different modifications to the baseline time series.
For example, imagine you are a clothing retailer who is considering an end of season sale to clear space for new styles. After creating a baseline forecast, you can use a what-if analysis to investigate how different sales tactics might affect your goals.
You could create a scenario where everything is given a 25% markdown, and another where everything is given a fixed dollar markdown. You could create a scenario where the sale lasts for one week and another where the sale lasts for one month. With a what-if analysis, you can compare many different scenarios against each other.
Note that a what-if analysis is meant to display what the forecasting model has learned and how it will behave in the scenarios that you are evaluating. Do not blindly use the results of the what-if analysis to make business decisions. For instance, forecasts might not be accurate for novel scenarios where there is no reference available to determine whether a forecast is good.
The TimeSeriesSelector object defines the items that you want in the what-if analysis.
', 'CreateWhatIfForecast' => 'A what-if forecast is a forecast that is created from a modified version of the baseline forecast. Each what-if forecast incorporates either a replacement dataset or a set of transformations to the original dataset.
', 'CreateWhatIfForecastExport' => 'Exports a forecast created by the CreateWhatIfForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket. The forecast file name will match the following conventions:
≈<ForecastExportJobName>_<ExportTimestamp>_<PartNumber>
The <ExportTimestamp> component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ).
You must specify a DataDestination object that includes an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles.
For more information, see howitworks-forecast.
To get a list of all your what-if forecast export jobs, use the ListWhatIfForecastExports operation.
The Status of the forecast export job must be ACTIVE before you can access the forecast in your Amazon S3 bucket. To get the status, use the DescribeWhatIfForecastExport operation.
Deletes an Amazon Forecast dataset that was created using the CreateDataset operation. You can only delete datasets that have a status of ACTIVE or CREATE_FAILED. To get the status use the DescribeDataset operation.
Forecast does not automatically update any dataset groups that contain the deleted dataset. In order to update the dataset group, use the UpdateDatasetGroup operation, omitting the deleted dataset\'s ARN.
Deletes a dataset group created using the CreateDatasetGroup operation. You can only delete dataset groups that have a status of ACTIVE, CREATE_FAILED, or UPDATE_FAILED. To get the status, use the DescribeDatasetGroup operation.
This operation deletes only the dataset group, not the datasets in the group.
', 'DeleteDatasetImportJob' => 'Deletes a dataset import job created using the CreateDatasetImportJob operation. You can delete only dataset import jobs that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeDatasetImportJob operation.
Deletes an Explainability resource.
You can delete only predictor that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeExplainability operation.
Deletes an Explainability export.
', 'DeleteForecast' => 'Deletes a forecast created using the CreateForecast operation. You can delete only forecasts that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeForecast operation.
You can\'t delete a forecast while it is being exported. After a forecast is deleted, you can no longer query the forecast.
', 'DeleteForecastExportJob' => 'Deletes a forecast export job created using the CreateForecastExportJob operation. You can delete only export jobs that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeForecastExportJob operation.
Deletes a monitor resource. You can only delete a monitor resource with a status of ACTIVE, ACTIVE_STOPPED, CREATE_FAILED, or CREATE_STOPPED.
Deletes a predictor created using the DescribePredictor or CreatePredictor operations. You can delete only predictor that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribePredictor operation.
Deletes a predictor backtest export job.
', 'DeleteResourceTree' => 'Deletes an entire resource tree. This operation will delete the parent resource and its child resources.
Child resources are resources that were created from another resource. For example, when a forecast is generated from a predictor, the forecast is the child resource and the predictor is the parent resource.
Amazon Forecast resources possess the following parent-child resource hierarchies:
Dataset: dataset import jobs
Dataset Group: predictors, predictor backtest export jobs, forecasts, forecast export jobs
Predictor: predictor backtest export jobs, forecasts, forecast export jobs
Forecast: forecast export jobs
DeleteResourceTree will only delete Amazon Forecast resources, and will not delete datasets or exported files stored in Amazon S3.
Deletes a what-if analysis created using the CreateWhatIfAnalysis operation. You can delete only what-if analyses that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeWhatIfAnalysis operation.
You can\'t delete a what-if analysis while any of its forecasts are being exported.
', 'DeleteWhatIfForecast' => 'Deletes a what-if forecast created using the CreateWhatIfForecast operation. You can delete only what-if forecasts that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeWhatIfForecast operation.
You can\'t delete a what-if forecast while it is being exported. After a what-if forecast is deleted, you can no longer query the what-if analysis.
', 'DeleteWhatIfForecastExport' => 'Deletes a what-if forecast export created using the CreateWhatIfForecastExport operation. You can delete only what-if forecast exports that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeWhatIfForecastExport operation.
Describes a predictor created using the CreateAutoPredictor operation.
', 'DescribeDataset' => 'Describes an Amazon Forecast dataset created using the CreateDataset operation.
In addition to listing the parameters specified in the CreateDataset request, this operation includes the following dataset properties:
CreationTime
LastModificationTime
Status
Describes a dataset group created using the CreateDatasetGroup operation.
In addition to listing the parameters provided in the CreateDatasetGroup request, this operation includes the following properties:
DatasetArns - The datasets belonging to the group.
CreationTime
LastModificationTime
Status
Describes a dataset import job created using the CreateDatasetImportJob operation.
In addition to listing the parameters provided in the CreateDatasetImportJob request, this operation includes the following properties:
CreationTime
LastModificationTime
DataSize
FieldStatistics
Status
Message - If an error occurred, information about the error.
Describes an Explainability resource created using the CreateExplainability operation.
', 'DescribeExplainabilityExport' => 'Describes an Explainability export created using the CreateExplainabilityExport operation.
', 'DescribeForecast' => 'Describes a forecast created using the CreateForecast operation.
In addition to listing the properties provided in the CreateForecast request, this operation lists the following properties:
DatasetGroupArn - The dataset group that provided the training data.
CreationTime
LastModificationTime
Status
Message - If an error occurred, information about the error.
Describes a forecast export job created using the CreateForecastExportJob operation.
In addition to listing the properties provided by the user in the CreateForecastExportJob request, this operation lists the following properties:
CreationTime
LastModificationTime
Status
Message - If an error occurred, information about the error.
Describes a monitor resource. In addition to listing the properties provided in the CreateMonitor request, this operation lists the following properties:
Baseline
CreationTime
LastEvaluationTime
LastEvaluationState
LastModificationTime
Message
Status
This operation is only valid for legacy predictors created with CreatePredictor. If you are not using a legacy predictor, use DescribeAutoPredictor.
Describes a predictor created using the CreatePredictor operation.
In addition to listing the properties provided in the CreatePredictor request, this operation lists the following properties:
DatasetImportJobArns - The dataset import jobs used to import training data.
AutoMLAlgorithmArns - If AutoML is performed, the algorithms that were evaluated.
CreationTime
LastModificationTime
Status
Message - If an error occurred, information about the error.
Describes a predictor backtest export job created using the CreatePredictorBacktestExportJob operation.
In addition to listing the properties provided by the user in the CreatePredictorBacktestExportJob request, this operation lists the following properties:
CreationTime
LastModificationTime
Status
Message (if an error occurred)
Describes the what-if analysis created using the CreateWhatIfAnalysis operation.
In addition to listing the properties provided in the CreateWhatIfAnalysis request, this operation lists the following properties:
CreationTime
LastModificationTime
Message - If an error occurred, information about the error.
Status
Describes the what-if forecast created using the CreateWhatIfForecast operation.
In addition to listing the properties provided in the CreateWhatIfForecast request, this operation lists the following properties:
CreationTime
LastModificationTime
Message - If an error occurred, information about the error.
Status
Describes the what-if forecast export created using the CreateWhatIfForecastExport operation.
In addition to listing the properties provided in the CreateWhatIfForecastExport request, this operation lists the following properties:
CreationTime
LastModificationTime
Message - If an error occurred, information about the error.
Status
Provides metrics on the accuracy of the models that were trained by the CreatePredictor operation. Use metrics to see how well the model performed and to decide whether to use the predictor to generate a forecast. For more information, see Predictor Metrics.
This operation generates metrics for each backtest window that was evaluated. The number of backtest windows (NumberOfBacktestWindows) is specified using the EvaluationParameters object, which is optionally included in the CreatePredictor request. If NumberOfBacktestWindows isn\'t specified, the number defaults to one.
The parameters of the filling method determine which items contribute to the metrics. If you want all items to contribute, specify zero. If you want only those items that have complete data in the range being evaluated to contribute, specify nan. For more information, see FeaturizationMethod.
Before you can get accuracy metrics, the Status of the predictor must be ACTIVE, signifying that training has completed. To get the status, use the DescribePredictor operation.
Returns a list of dataset groups created using the CreateDatasetGroup operation. For each dataset group, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the dataset group ARN with the DescribeDatasetGroup operation.
', 'ListDatasetImportJobs' => 'Returns a list of dataset import jobs created using the CreateDatasetImportJob operation. For each import job, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the ARN with the DescribeDatasetImportJob operation. You can filter the list by providing an array of Filter objects.
', 'ListDatasets' => 'Returns a list of datasets created using the CreateDataset operation. For each dataset, a summary of its properties, including its Amazon Resource Name (ARN), is returned. To retrieve the complete set of properties, use the ARN with the DescribeDataset operation.
', 'ListExplainabilities' => 'Returns a list of Explainability resources created using the CreateExplainability operation. This operation returns a summary for each Explainability. You can filter the list using an array of Filter objects.
To retrieve the complete set of properties for a particular Explainability resource, use the ARN with the DescribeExplainability operation.
', 'ListExplainabilityExports' => 'Returns a list of Explainability exports created using the CreateExplainabilityExport operation. This operation returns a summary for each Explainability export. You can filter the list using an array of Filter objects.
To retrieve the complete set of properties for a particular Explainability export, use the ARN with the DescribeExplainability operation.
', 'ListForecastExportJobs' => 'Returns a list of forecast export jobs created using the CreateForecastExportJob operation. For each forecast export job, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). To retrieve the complete set of properties, use the ARN with the DescribeForecastExportJob operation. You can filter the list using an array of Filter objects.
', 'ListForecasts' => 'Returns a list of forecasts created using the CreateForecast operation. For each forecast, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). To retrieve the complete set of properties, specify the ARN with the DescribeForecast operation. You can filter the list using an array of Filter objects.
', 'ListMonitorEvaluations' => 'Returns a list of the monitoring evaluation results and predictor events collected by the monitor resource during different windows of time.
For information about monitoring see predictor-monitoring. For more information about retrieving monitoring results see Viewing Monitoring Results.
', 'ListMonitors' => 'Returns a list of monitors created with the CreateMonitor operation and CreateAutoPredictor operation. For each monitor resource, this operation returns of a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve a complete set of properties of a monitor resource by specify the monitor\'s ARN in the DescribeMonitor operation.
', 'ListPredictorBacktestExportJobs' => 'Returns a list of predictor backtest export jobs created using the CreatePredictorBacktestExportJob operation. This operation returns a summary for each backtest export job. You can filter the list using an array of Filter objects.
To retrieve the complete set of properties for a particular backtest export job, use the ARN with the DescribePredictorBacktestExportJob operation.
', 'ListPredictors' => 'Returns a list of predictors created using the CreateAutoPredictor or CreatePredictor operations. For each predictor, this operation returns a summary of its properties, including its Amazon Resource Name (ARN).
You can retrieve the complete set of properties by using the ARN with the DescribeAutoPredictor and DescribePredictor operations. You can filter the list using an array of Filter objects.
', 'ListTagsForResource' => 'Lists the tags for an Amazon Forecast resource.
', 'ListWhatIfAnalyses' => 'Returns a list of what-if analyses created using the CreateWhatIfAnalysis operation. For each what-if analysis, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the what-if analysis ARN with the DescribeWhatIfAnalysis operation.
', 'ListWhatIfForecastExports' => 'Returns a list of what-if forecast exports created using the CreateWhatIfForecastExport operation. For each what-if forecast export, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the what-if forecast export ARN with the DescribeWhatIfForecastExport operation.
', 'ListWhatIfForecasts' => 'Returns a list of what-if forecasts created using the CreateWhatIfForecast operation. For each what-if forecast, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the what-if forecast ARN with the DescribeWhatIfForecast operation.
', 'ResumeResource' => 'Resumes a stopped monitor resource.
', 'StopResource' => 'Stops a resource.
The resource undergoes the following states: CREATE_STOPPING and CREATE_STOPPED. You cannot resume a resource once it has been stopped.
This operation can be applied to the following resources (and their corresponding child resources):
Dataset Import Job
Predictor Job
Forecast Job
Forecast Export Job
Predictor Backtest Export Job
Explainability Job
Explainability Export Job
Associates the specified tags to a resource with the specified resourceArn. If existing tags on a resource are not specified in the request parameters, they are not changed. When a resource is deleted, the tags associated with that resource are also deleted.
Deletes the specified tags from a resource.
', 'UpdateDatasetGroup' => 'Replaces the datasets in a dataset group with the specified datasets.
The Status of the dataset group must be ACTIVE before you can use the dataset group to create a predictor. Use the DescribeDatasetGroup operation to get the status.
Defines the modifications that you are making to an attribute for a what-if forecast. For example, you can use this operation to create a what-if forecast that investigates a 10% off sale on all shoes. To do this, you specify "AttributeName": "shoes", "Operation": "MULTIPLY", and "Value": "0.90". Pair this operation with the TimeSeriesCondition operation within the CreateWhatIfForecastRequest$TimeSeriesTransformations operation to define a subset of attribute items that are modified.
An array of actions that define a time series and how it is transformed. These transformations create a new time series that is used for the what-if analysis.
', ], ], 'AdditionalDataset' => [ 'base' => 'Describes an additional dataset. This object is part of the DataConfig object. Forecast supports the Weather Index and Holidays additional datasets.
Weather Index
The Amazon Forecast Weather Index is a built-in dataset that incorporates historical and projected weather information into your model. The Weather Index supplements your datasets with over two years of historical weather data and up to 14 days of projected weather data. For more information, see Amazon Forecast Weather Index.
Holidays
Holidays is a built-in dataset that incorporates national holiday information into your model. It provides native support for the holiday calendars of 66 countries. To view the holiday calendars, refer to the Jollyday library. For more information, see Holidays Featurization.
', 'refs' => [ 'AdditionalDatasets$member' => NULL, ], ], 'AdditionalDatasets' => [ 'base' => NULL, 'refs' => [ 'DataConfig$AdditionalDatasets' => 'Additional built-in datasets like Holidays and the Weather Index.
', ], ], 'Arn' => [ 'base' => NULL, 'refs' => [ 'ArnList$member' => NULL, 'CreateAutoPredictorRequest$ReferencePredictorArn' => 'The ARN of the predictor to retrain or upgrade. This parameter is only used when retraining or upgrading a predictor. When creating a new predictor, do not specify a value for this parameter.
When upgrading or retraining a predictor, only specify values for the ReferencePredictorArn and PredictorName. The value for PredictorName must be a unique predictor name.
The Amazon Resource Name (ARN) of the predictor.
', 'CreateDatasetGroupResponse$DatasetGroupArn' => 'The Amazon Resource Name (ARN) of the dataset group.
', 'CreateDatasetImportJobRequest$DatasetArn' => 'The Amazon Resource Name (ARN) of the Amazon Forecast dataset that you want to import data to.
', 'CreateDatasetImportJobResponse$DatasetImportJobArn' => 'The Amazon Resource Name (ARN) of the dataset import job.
', 'CreateDatasetResponse$DatasetArn' => 'The Amazon Resource Name (ARN) of the dataset.
', 'CreateExplainabilityExportRequest$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explainability to export.
', 'CreateExplainabilityExportResponse$ExplainabilityExportArn' => 'The Amazon Resource Name (ARN) of the export.
', 'CreateExplainabilityRequest$ResourceArn' => 'The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.
', 'CreateExplainabilityResponse$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explainability.
', 'CreateForecastExportJobRequest$ForecastArn' => 'The Amazon Resource Name (ARN) of the forecast that you want to export.
', 'CreateForecastExportJobResponse$ForecastExportJobArn' => 'The Amazon Resource Name (ARN) of the export job.
', 'CreateForecastRequest$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor to use to generate the forecast.
', 'CreateForecastResponse$ForecastArn' => 'The Amazon Resource Name (ARN) of the forecast.
', 'CreateMonitorRequest$ResourceArn' => 'The Amazon Resource Name (ARN) of the predictor to monitor.
', 'CreateMonitorResponse$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource.
', 'CreatePredictorBacktestExportJobRequest$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor that you want to export.
', 'CreatePredictorBacktestExportJobResponse$PredictorBacktestExportJobArn' => 'The Amazon Resource Name (ARN) of the predictor backtest export job that you want to export.
', 'CreatePredictorRequest$AlgorithmArn' => 'The Amazon Resource Name (ARN) of the algorithm to use for model training. Required if PerformAutoML is not set to true.
Supported algorithms:
arn:aws:forecast:::algorithm/ARIMA
arn:aws:forecast:::algorithm/CNN-QR
arn:aws:forecast:::algorithm/Deep_AR_Plus
arn:aws:forecast:::algorithm/ETS
arn:aws:forecast:::algorithm/NPTS
arn:aws:forecast:::algorithm/Prophet
The Amazon Resource Name (ARN) of the predictor.
', 'CreateWhatIfAnalysisRequest$ForecastArn' => 'The Amazon Resource Name (ARN) of the baseline forecast.
', 'CreateWhatIfAnalysisResponse$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis.
', 'CreateWhatIfForecastRequest$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis.
', 'DataConfig$DatasetGroupArn' => 'The ARN of the dataset group used to train the predictor.
', 'DatasetGroupSummary$DatasetGroupArn' => 'The Amazon Resource Name (ARN) of the dataset group.
', 'DatasetImportJobSummary$DatasetImportJobArn' => 'The Amazon Resource Name (ARN) of the dataset import job.
', 'DatasetSummary$DatasetArn' => 'The Amazon Resource Name (ARN) of the dataset.
', 'DeleteDatasetGroupRequest$DatasetGroupArn' => 'The Amazon Resource Name (ARN) of the dataset group to delete.
', 'DeleteDatasetImportJobRequest$DatasetImportJobArn' => 'The Amazon Resource Name (ARN) of the dataset import job to delete.
', 'DeleteDatasetRequest$DatasetArn' => 'The Amazon Resource Name (ARN) of the dataset to delete.
', 'DeleteExplainabilityExportRequest$ExplainabilityExportArn' => 'The Amazon Resource Name (ARN) of the Explainability export to delete.
', 'DeleteExplainabilityRequest$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explainability resource to delete.
', 'DeleteForecastExportJobRequest$ForecastExportJobArn' => 'The Amazon Resource Name (ARN) of the forecast export job to delete.
', 'DeleteForecastRequest$ForecastArn' => 'The Amazon Resource Name (ARN) of the forecast to delete.
', 'DeleteMonitorRequest$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource to delete.
', 'DeletePredictorBacktestExportJobRequest$PredictorBacktestExportJobArn' => 'The Amazon Resource Name (ARN) of the predictor backtest export job to delete.
', 'DeletePredictorRequest$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor to delete.
', 'DeleteResourceTreeRequest$ResourceArn' => 'The Amazon Resource Name (ARN) of the parent resource to delete. All child resources of the parent resource will also be deleted.
', 'DeleteWhatIfAnalysisRequest$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis that you want to delete.
', 'DescribeAutoPredictorRequest$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor.
', 'DescribeAutoPredictorResponse$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor
', 'DescribeDatasetGroupRequest$DatasetGroupArn' => 'The Amazon Resource Name (ARN) of the dataset group.
', 'DescribeDatasetGroupResponse$DatasetGroupArn' => 'The ARN of the dataset group.
', 'DescribeDatasetImportJobRequest$DatasetImportJobArn' => 'The Amazon Resource Name (ARN) of the dataset import job.
', 'DescribeDatasetImportJobResponse$DatasetImportJobArn' => 'The ARN of the dataset import job.
', 'DescribeDatasetImportJobResponse$DatasetArn' => 'The Amazon Resource Name (ARN) of the dataset that the training data was imported to.
', 'DescribeDatasetRequest$DatasetArn' => 'The Amazon Resource Name (ARN) of the dataset.
', 'DescribeDatasetResponse$DatasetArn' => 'The Amazon Resource Name (ARN) of the dataset.
', 'DescribeExplainabilityExportRequest$ExplainabilityExportArn' => 'The Amazon Resource Name (ARN) of the Explainability export.
', 'DescribeExplainabilityExportResponse$ExplainabilityExportArn' => 'The Amazon Resource Name (ARN) of the Explainability export.
', 'DescribeExplainabilityExportResponse$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explainability export.
', 'DescribeExplainabilityRequest$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explaianability to describe.
', 'DescribeExplainabilityResponse$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explainability.
', 'DescribeExplainabilityResponse$ResourceArn' => 'The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability resource.
', 'DescribeForecastExportJobRequest$ForecastExportJobArn' => 'The Amazon Resource Name (ARN) of the forecast export job.
', 'DescribeForecastExportJobResponse$ForecastExportJobArn' => 'The ARN of the forecast export job.
', 'DescribeForecastExportJobResponse$ForecastArn' => 'The Amazon Resource Name (ARN) of the exported forecast.
', 'DescribeForecastRequest$ForecastArn' => 'The Amazon Resource Name (ARN) of the forecast.
', 'DescribeForecastResponse$ForecastArn' => 'The forecast ARN as specified in the request.
', 'DescribeForecastResponse$PredictorArn' => 'The ARN of the predictor used to generate the forecast.
', 'DescribeForecastResponse$DatasetGroupArn' => 'The ARN of the dataset group that provided the data used to train the predictor.
', 'DescribeMonitorRequest$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource to describe.
', 'DescribeMonitorResponse$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource described.
', 'DescribeMonitorResponse$ResourceArn' => 'The Amazon Resource Name (ARN) of the auto predictor being monitored.
', 'DescribePredictorBacktestExportJobRequest$PredictorBacktestExportJobArn' => 'The Amazon Resource Name (ARN) of the predictor backtest export job.
', 'DescribePredictorBacktestExportJobResponse$PredictorBacktestExportJobArn' => 'The Amazon Resource Name (ARN) of the predictor backtest export job.
', 'DescribePredictorBacktestExportJobResponse$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor.
', 'DescribePredictorRequest$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor that you want information about.
', 'DescribePredictorResponse$AlgorithmArn' => 'The Amazon Resource Name (ARN) of the algorithm used for model training.
', 'DescribeWhatIfAnalysisRequest$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis that you are interested in.
', 'DescribeWhatIfAnalysisResponse$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis.
', 'DescribeWhatIfAnalysisResponse$ForecastArn' => 'The Amazon Resource Name (ARN) of the what-if forecast.
', 'DescribeWhatIfForecastResponse$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis that contains this forecast.
', 'EncryptionConfig$RoleArn' => 'The ARN of the IAM role that Amazon Forecast can assume to access the KMS key.
Passing a role across Amazon Web Services accounts is not allowed. If you pass a role that isn\'t in your account, you get an InvalidInputException error.
The Amazon Resource Name (ARN) of the algorithm that was evaluated.
', 'ExplainabilityExportSummary$ExplainabilityExportArn' => 'The Amazon Resource Name (ARN) of the Explainability export.
', 'ExplainabilityInfo$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explainability.
', 'ExplainabilitySummary$ExplainabilityArn' => 'The Amazon Resource Name (ARN) of the Explainability.
', 'ExplainabilitySummary$ResourceArn' => 'The Amazon Resource Name (ARN) of the Predictor or Forecast used to create the Explainability.
', 'Filter$Value' => 'The value to match.
', 'ForecastExportJobSummary$ForecastExportJobArn' => 'The Amazon Resource Name (ARN) of the forecast export job.
', 'ForecastSummary$ForecastArn' => 'The ARN of the forecast.
', 'GetAccuracyMetricsRequest$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor to get metrics for.
', 'InputDataConfig$DatasetGroupArn' => 'The Amazon Resource Name (ARN) of the dataset group.
', 'ListMonitorEvaluationsRequest$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource to get results from.
', 'ListTagsForResourceRequest$ResourceArn' => 'The Amazon Resource Name (ARN) that identifies the resource for which to list the tags.
', 'MonitorDataSource$DatasetImportJobArn' => 'The Amazon Resource Name (ARN) of the dataset import job used to import the data that initiated the monitor evaluation.
', 'MonitorDataSource$ForecastArn' => 'The Amazon Resource Name (ARN) of the forecast the monitor used during the evaluation.
', 'MonitorDataSource$PredictorArn' => 'The Amazon Resource Name (ARN) of the predictor resource you are monitoring.
', 'MonitorInfo$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource.
', 'MonitorSummary$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource.
', 'MonitorSummary$ResourceArn' => 'The Amazon Resource Name (ARN) of the predictor being monitored.
', 'PredictorBacktestExportJobSummary$PredictorBacktestExportJobArn' => 'The Amazon Resource Name (ARN) of the predictor backtest export job.
', 'PredictorExecution$AlgorithmArn' => 'The ARN of the algorithm used to test the predictor.
', 'PredictorMonitorEvaluation$ResourceArn' => 'The Amazon Resource Name (ARN) of the resource to monitor.
', 'PredictorMonitorEvaluation$MonitorArn' => 'The Amazon Resource Name (ARN) of the monitor resource.
', 'PredictorSummary$PredictorArn' => 'The ARN of the predictor.
', 'PredictorSummary$DatasetGroupArn' => 'The Amazon Resource Name (ARN) of the dataset group that contains the data used to train the predictor.
', 'ReferencePredictorSummary$Arn' => 'The ARN of the reference predictor.
', 'ResumeResourceRequest$ResourceArn' => 'The Amazon Resource Name (ARN) of the monitor resource to resume.
', 'S3Config$RoleArn' => 'The ARN of the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket or files. If you provide a value for the KMSKeyArn key, the role must allow access to the key.
Passing a role across Amazon Web Services accounts is not allowed. If you pass a role that isn\'t in your account, you get an InvalidInputException error.
The Amazon Resource Name (ARN) that identifies the resource to stop. The supported ARNs are DatasetImportJobArn, PredictorArn, PredictorBacktestExportJobArn, ForecastArn, ForecastExportJobArn, ExplainabilityArn, and ExplainabilityExportArn.
The Amazon Resource Name (ARN) that identifies the resource for which to list the tags.
', 'UntagResourceRequest$ResourceArn' => 'The Amazon Resource Name (ARN) that identifies the resource for which to list the tags.
', 'UpdateDatasetGroupRequest$DatasetGroupArn' => 'The ARN of the dataset group.
', 'WhatIfAnalysisSummary$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis.
', 'WhatIfAnalysisSummary$ForecastArn' => 'The Amazon Resource Name (ARN) of the baseline forecast that is being used in this what-if analysis.
', 'WhatIfForecastSummary$WhatIfAnalysisArn' => 'The Amazon Resource Name (ARN) of the what-if analysis that contains this what-if forecast.
', ], ], 'ArnList' => [ 'base' => NULL, 'refs' => [ 'CreateDatasetGroupRequest$DatasetArns' => 'An array of Amazon Resource Names (ARNs) of the datasets that you want to include in the dataset group.
', 'DescribeAutoPredictorResponse$DatasetImportJobArns' => 'An array of the ARNs of the dataset import jobs used to import training data for the predictor.
', 'DescribeDatasetGroupResponse$DatasetArns' => 'An array of Amazon Resource Names (ARNs) of the datasets contained in the dataset group.
', 'DescribePredictorResponse$AutoMLAlgorithmArns' => 'When PerformAutoML is specified, the ARN of the chosen algorithm.
An array of the ARNs of the dataset import jobs used to import training data for the predictor.
', 'UpdateDatasetGroupRequest$DatasetArns' => 'An array of the Amazon Resource Names (ARNs) of the datasets to add to the dataset group.
', ], ], 'AttributeConfig' => [ 'base' => 'Provides information about the method used to transform attributes.
The following is an example using the RETAIL domain:
{
"AttributeName": "demand",
"Transformations": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}
}
Aggregation and filling options for attributes in your dataset group.
', ], ], 'AttributeType' => [ 'base' => NULL, 'refs' => [ 'SchemaAttribute$AttributeType' => 'The data type of the field.
For a related time series dataset, other than date, item_id, and forecast dimensions attributes, all attributes should be of numerical type (integer/float).
', ], ], 'AttributeValue' => [ 'base' => NULL, 'refs' => [ 'TimeSeriesCondition$AttributeValue' => 'The value that is applied for the chosen Condition.
The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.
Used to overide the default AutoML strategy, which is to optimize predictor accuracy. To apply an AutoML strategy that minimizes training time, use LatencyOptimized.
This parameter is only valid for predictors trained using AutoML.
', 'DescribePredictorResponse$AutoMLOverrideStrategy' => ' The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.
The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.
This parameter is only valid for predictors trained using AutoML.
', 'GetAccuracyMetricsResponse$AutoMLOverrideStrategy' => ' The LatencyOptimized AutoML override strategy is only available in private beta. Contact Amazon Web Services Support or your account manager to learn more about access privileges.
The AutoML strategy used to train the predictor. Unless LatencyOptimized is specified, the AutoML strategy optimizes predictor accuracy.
This parameter is only valid for predictors trained using AutoML.
', ], ], 'Baseline' => [ 'base' => 'Metrics you can use as a baseline for comparison purposes. Use these metrics when you interpret monitoring results for an auto predictor.
', 'refs' => [ 'DescribeMonitorResponse$Baseline' => 'Metrics you can use as a baseline for comparison purposes. Use these values you interpret monitoring results for an auto predictor.
', ], ], 'BaselineMetric' => [ 'base' => 'An individual metric that you can use for comparison as you evaluate your monitoring results.
', 'refs' => [ 'BaselineMetrics$member' => NULL, ], ], 'BaselineMetrics' => [ 'base' => NULL, 'refs' => [ 'PredictorBaseline$BaselineMetrics' => 'The initial accuracy metrics for the predictor. Use these metrics as a baseline for comparison purposes as you use your predictor and the metrics change.
', ], ], 'Boolean' => [ 'base' => NULL, 'refs' => [ 'CreateAutoPredictorRequest$ExplainPredictor' => 'Create an Explainability resource for the predictor.
', 'CreateExplainabilityRequest$EnableVisualization' => 'Create an Explainability visualization that is viewable within the Amazon Web Services console.
', 'CreatePredictorRequest$PerformAutoML' => 'Whether to perform AutoML. When Amazon Forecast performs AutoML, it evaluates the algorithms it provides and chooses the best algorithm and configuration for your training dataset.
The default value is false. In this case, you are required to specify an algorithm.
Set PerformAutoML to true to have Amazon Forecast perform AutoML. This is a good option if you aren\'t sure which algorithm is suitable for your training data. In this case, PerformHPO must be false.
Whether to perform hyperparameter optimization (HPO). HPO finds optimal hyperparameter values for your training data. The process of performing HPO is known as running a hyperparameter tuning job.
The default value is false. In this case, Amazon Forecast uses default hyperparameter values from the chosen algorithm.
To override the default values, set PerformHPO to true and, optionally, supply the HyperParameterTuningJobConfig object. The tuning job specifies a metric to optimize, which hyperparameters participate in tuning, and the valid range for each tunable hyperparameter. In this case, you are required to specify an algorithm and PerformAutoML must be false.
The following algorithms support HPO:
DeepAR+
CNN-QR
Whether the visualization was enabled for the Explainability resource.
', 'DescribePredictorResponse$PerformAutoML' => 'Whether the predictor is set to perform AutoML.
', 'DescribePredictorResponse$PerformHPO' => 'Whether the predictor is set to perform hyperparameter optimization (HPO).
', 'DescribePredictorResponse$IsAutoPredictor' => 'Whether the predictor was created with CreateAutoPredictor.
', 'ForecastSummary$CreatedUsingAutoPredictor' => 'Whether the Forecast was created from an AutoPredictor.
', 'GetAccuracyMetricsResponse$IsAutoPredictor' => 'Whether the predictor was created with CreateAutoPredictor.
', 'PredictorSummary$IsAutoPredictor' => 'Whether AutoPredictor was used to create the predictor.
', ], ], 'CategoricalParameterRange' => [ 'base' => 'Specifies a categorical hyperparameter and it\'s range of tunable values. This object is part of the ParameterRanges object.
', 'refs' => [ 'CategoricalParameterRanges$member' => NULL, ], ], 'CategoricalParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$CategoricalParameterRanges' => 'Specifies the tunable range for each categorical hyperparameter.
', ], ], 'Condition' => [ 'base' => NULL, 'refs' => [ 'TimeSeriesCondition$Condition' => 'The condition to apply. Valid values are EQUALS, NOT_EQUALS, LESS_THAN and GREATER_THAN.
Weather Index
To enable the Weather Index, do not specify a value for Configuration.
Holidays
Holidays
To enable Holidays, set CountryCode to one of the following two-letter country codes:
"AL" - ALBANIA
"AR" - ARGENTINA
"AT" - AUSTRIA
"AU" - AUSTRALIA
"BA" - BOSNIA HERZEGOVINA
"BE" - BELGIUM
"BG" - BULGARIA
"BO" - BOLIVIA
"BR" - BRAZIL
"BY" - BELARUS
"CA" - CANADA
"CL" - CHILE
"CO" - COLOMBIA
"CR" - COSTA RICA
"HR" - CROATIA
"CZ" - CZECH REPUBLIC
"DK" - DENMARK
"EC" - ECUADOR
"EE" - ESTONIA
"ET" - ETHIOPIA
"FI" - FINLAND
"FR" - FRANCE
"DE" - GERMANY
"GR" - GREECE
"HU" - HUNGARY
"IS" - ICELAND
"IN" - INDIA
"IE" - IRELAND
"IT" - ITALY
"JP" - JAPAN
"KZ" - KAZAKHSTAN
"KR" - KOREA
"LV" - LATVIA
"LI" - LIECHTENSTEIN
"LT" - LITHUANIA
"LU" - LUXEMBOURG
"MK" - MACEDONIA
"MT" - MALTA
"MX" - MEXICO
"MD" - MOLDOVA
"ME" - MONTENEGRO
"NL" - NETHERLANDS
"NZ" - NEW ZEALAND
"NI" - NICARAGUA
"NG" - NIGERIA
"NO" - NORWAY
"PA" - PANAMA
"PY" - PARAGUAY
"PE" - PERU
"PL" - POLAND
"PT" - PORTUGAL
"RO" - ROMANIA
"RU" - RUSSIA
"RS" - SERBIA
"SK" - SLOVAKIA
"SI" - SLOVENIA
"ZA" - SOUTH AFRICA
"ES" - SPAIN
"SE" - SWEDEN
"CH" - SWITZERLAND
"UA" - UKRAINE
"AE" - UNITED ARAB EMIRATES
"US" - UNITED STATES
"UK" - UNITED KINGDOM
"UY" - URUGUAY
"VE" - VENEZUELA
Specifies a continuous hyperparameter and it\'s range of tunable values. This object is part of the ParameterRanges object.
', 'refs' => [ 'ContinuousParameterRanges$member' => NULL, ], ], 'ContinuousParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$ContinuousParameterRanges' => 'Specifies the tunable range for each continuous hyperparameter.
', ], ], 'CreateAutoPredictorRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateAutoPredictorResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateDatasetGroupRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateDatasetGroupResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateDatasetImportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateDatasetImportJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateDatasetRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateDatasetResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateExplainabilityExportRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateExplainabilityExportResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateExplainabilityRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateExplainabilityResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateForecastExportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateForecastExportJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateForecastRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateForecastResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateMonitorRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateMonitorResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreatePredictorBacktestExportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreatePredictorBacktestExportJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreatePredictorRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreatePredictorResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateWhatIfAnalysisRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateWhatIfAnalysisResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateWhatIfForecastExportRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateWhatIfForecastExportResponse' => [ 'base' => NULL, 'refs' => [], ], 'CreateWhatIfForecastRequest' => [ 'base' => NULL, 'refs' => [], ], 'CreateWhatIfForecastResponse' => [ 'base' => NULL, 'refs' => [], ], 'DataConfig' => [ 'base' => 'The data configuration for your dataset group and any additional datasets.
', 'refs' => [ 'CreateAutoPredictorRequest$DataConfig' => 'The data configuration for your dataset group and any additional datasets.
', 'DescribeAutoPredictorResponse$DataConfig' => 'The data configuration for your dataset group and any additional datasets.
', ], ], 'DataDestination' => [ 'base' => 'The destination for an export job. Provide an S3 path, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the location, and an Key Management Service (KMS) key (optional).
', 'refs' => [ 'CreateExplainabilityExportRequest$Destination' => NULL, 'CreateForecastExportJobRequest$Destination' => 'The location where you want to save the forecast and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the location. The forecast must be exported to an Amazon S3 bucket.
If encryption is used, Destination must include an Key Management Service (KMS) key. The IAM role must allow Amazon Forecast permission to access the key.
The location where you want to save the forecast and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the location. The forecast must be exported to an Amazon S3 bucket.
If encryption is used, Destination must include an Key Management Service (KMS) key. The IAM role must allow Amazon Forecast permission to access the key.
The path to the Amazon Simple Storage Service (Amazon S3) bucket where the forecast is exported.
', 'DescribePredictorBacktestExportJobResponse$Destination' => NULL, 'DescribeWhatIfForecastExportResponse$Destination' => NULL, 'ExplainabilityExportSummary$Destination' => NULL, 'ForecastExportJobSummary$Destination' => 'The path to the Amazon Simple Storage Service (Amazon S3) bucket where the forecast is exported.
', 'PredictorBacktestExportJobSummary$Destination' => NULL, 'WhatIfForecastExportSummary$Destination' => 'The path to the Amazon Simple Storage Service (Amazon S3) bucket where the forecast is exported.
', ], ], 'DataSource' => [ 'base' => 'The source of your data, an Identity and Access Management (IAM) role that allows Amazon Forecast to access the data and, optionally, an Key Management Service (KMS) key.
', 'refs' => [ 'CreateDatasetImportJobRequest$DataSource' => 'The location of the training data to import and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data. The training data must be stored in an Amazon S3 bucket.
If encryption is used, DataSource must include an Key Management Service (KMS) key and the IAM role must allow Amazon Forecast permission to access the key. The KMS key and IAM role must match those specified in the EncryptionConfig parameter of the CreateDataset operation.
The location of the training data to import and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data. The training data must be stored in an Amazon S3 bucket.
If encryption is used, DataSource includes an Key Management Service (KMS) key.
The location of the training data to import and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data.
If encryption is used, DataSource includes an Key Management Service (KMS) key.
Provides a summary of the dataset group properties used in the ListDatasetGroups operation. To get the complete set of properties, call the DescribeDatasetGroup operation, and provide the DatasetGroupArn.
An array of objects that summarize each dataset group\'s properties.
', ], ], 'DatasetImportJobSummary' => [ 'base' => 'Provides a summary of the dataset import job properties used in the ListDatasetImportJobs operation. To get the complete set of properties, call the DescribeDatasetImportJob operation, and provide the DatasetImportJobArn.
An array of objects that summarize each dataset import job\'s properties.
', ], ], 'DatasetSummary' => [ 'base' => 'Provides a summary of the dataset properties used in the ListDatasets operation. To get the complete set of properties, call the DescribeDataset operation, and provide the DatasetArn.
The dataset type. Valid values depend on the chosen Domain.
The dataset type.
', 'DescribeDatasetResponse$DatasetType' => 'The dataset type.
', ], ], 'Datasets' => [ 'base' => NULL, 'refs' => [ 'ListDatasetsResponse$Datasets' => 'An array of objects that summarize each dataset\'s properties.
', ], ], 'DayOfMonth' => [ 'base' => NULL, 'refs' => [ 'TimeAlignmentBoundary$DayOfMonth' => 'The day of the month to use for time alignment during aggregation.
', ], ], 'DayOfWeek' => [ 'base' => NULL, 'refs' => [ 'TimeAlignmentBoundary$DayOfWeek' => 'The day of week to use for time alignment during aggregation. The day must be in uppercase.
', ], ], 'DeleteDatasetGroupRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteDatasetImportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteDatasetRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteExplainabilityExportRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteExplainabilityRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteForecastExportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteForecastRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteMonitorRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeletePredictorBacktestExportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeletePredictorRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteResourceTreeRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteWhatIfAnalysisRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteWhatIfForecastExportRequest' => [ 'base' => NULL, 'refs' => [], ], 'DeleteWhatIfForecastRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeAutoPredictorRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeAutoPredictorResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeDatasetGroupRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeDatasetGroupResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeDatasetImportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeDatasetImportJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeDatasetRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeDatasetResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeExplainabilityExportRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeExplainabilityExportResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeExplainabilityRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeExplainabilityResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeForecastExportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeForecastExportJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeForecastRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeForecastResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeMonitorRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeMonitorResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribePredictorBacktestExportJobRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribePredictorBacktestExportJobResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribePredictorRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribePredictorResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWhatIfAnalysisRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWhatIfAnalysisResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWhatIfForecastExportRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWhatIfForecastExportResponse' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWhatIfForecastRequest' => [ 'base' => NULL, 'refs' => [], ], 'DescribeWhatIfForecastResponse' => [ 'base' => NULL, 'refs' => [], ], 'Detail' => [ 'base' => NULL, 'refs' => [ 'PredictorEvent$Detail' => 'The type of event. For example, Retrain. A retraining event denotes the timepoint when a predictor was retrained. Any monitor results from before the Datetime are from the previous predictor. Any new metrics are for the newly retrained predictor.
The domain associated with the dataset group. When you add a dataset to a dataset group, this value and the value specified for the Domain parameter of the CreateDataset operation must match.
The Domain and DatasetType that you choose determine the fields that must be present in training data that you import to a dataset. For example, if you choose the RETAIL domain and TARGET_TIME_SERIES as the DatasetType, Amazon Forecast requires that item_id, timestamp, and demand fields are present in your data. For more information, see Dataset groups.
The domain associated with the dataset. When you add a dataset to a dataset group, this value and the value specified for the Domain parameter of the CreateDatasetGroup operation must match.
The Domain and DatasetType that you choose determine the fields that must be present in the training data that you import to the dataset. For example, if you choose the RETAIL domain and TARGET_TIME_SERIES as the DatasetType, Amazon Forecast requires item_id, timestamp, and demand fields to be present in your data. For more information, see Importing datasets.
The domain associated with the dataset.
', 'DescribeDatasetGroupResponse$Domain' => 'The domain associated with the dataset group.
', 'DescribeDatasetResponse$Domain' => 'The domain associated with the dataset.
', ], ], 'Double' => [ 'base' => NULL, 'refs' => [ 'Action$Value' => 'The value that is applied for the chosen Operation.
The value for the metric.
', 'ContinuousParameterRange$MaxValue' => 'The maximum tunable value of the hyperparameter.
', 'ContinuousParameterRange$MinValue' => 'The minimum tunable value of the hyperparameter.
', 'DescribeDatasetImportJobResponse$DataSize' => 'The size of the dataset in gigabytes (GB) after the import job has finished.
', 'ErrorMetric$WAPE' => 'The weighted absolute percentage error (WAPE).
', 'ErrorMetric$RMSE' => 'The root-mean-square error (RMSE).
', 'ErrorMetric$MASE' => 'The Mean Absolute Scaled Error (MASE)
', 'ErrorMetric$MAPE' => 'The Mean Absolute Percentage Error (MAPE)
', 'MetricResult$MetricValue' => 'The value for the metric.
', 'Metrics$RMSE' => 'The root-mean-square error (RMSE).
', 'Metrics$AverageWeightedQuantileLoss' => 'The average value of all weighted quantile losses.
', 'Statistics$Avg' => 'For a numeric field, the average value in the field.
', 'Statistics$Stddev' => 'For a numeric field, the standard deviation.
', 'WeightedQuantileLoss$Quantile' => 'The quantile. Quantiles divide a probability distribution into regions of equal probability. For example, if the distribution was divided into 5 regions of equal probability, the quantiles would be 0.2, 0.4, 0.6, and 0.8.
', 'WeightedQuantileLoss$LossValue' => 'The difference between the predicted value and the actual value over the quantile, weighted (normalized) by dividing by the sum over all quantiles.
', ], ], 'EncryptionConfig' => [ 'base' => 'An Key Management Service (KMS) key and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key. You can specify this optional object in the CreateDataset and CreatePredictor requests.
', 'refs' => [ 'CreateAutoPredictorRequest$EncryptionConfig' => NULL, 'CreateDatasetRequest$EncryptionConfig' => 'An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
', 'CreatePredictorRequest$EncryptionConfig' => 'An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
', 'DescribeAutoPredictorResponse$EncryptionConfig' => NULL, 'DescribeDatasetResponse$EncryptionConfig' => 'The Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
', 'DescribePredictorResponse$EncryptionConfig' => 'An Key Management Service (KMS) key and the Identity and Access Management (IAM) role that Amazon Forecast can assume to access the key.
', ], ], 'ErrorMessage' => [ 'base' => NULL, 'refs' => [ 'DatasetImportJobSummary$Message' => 'If an error occurred, an informational message about the error.
', 'DescribeForecastResponse$Message' => 'If an error occurred, an informational message about the error.
', 'DescribeWhatIfAnalysisResponse$Message' => 'If an error occurred, an informational message about the error.
', 'DescribeWhatIfForecastResponse$Message' => 'If an error occurred, an informational message about the error.
', 'ExplainabilityExportSummary$Message' => 'Information about any errors that may have occurred during the Explainability export.
', 'ForecastExportJobSummary$Message' => 'If an error occurred, an informational message about the error.
', 'ForecastSummary$Message' => 'If an error occurred, an informational message about the error.
', 'InvalidInputException$Message' => NULL, 'InvalidNextTokenException$Message' => NULL, 'LimitExceededException$Message' => NULL, 'PredictorBacktestExportJobSummary$Message' => 'Information about any errors that may have occurred during the backtest export.
', 'PredictorSummary$Message' => 'If an error occurred, an informational message about the error.
', 'ResourceAlreadyExistsException$Message' => NULL, 'ResourceInUseException$Message' => NULL, 'ResourceNotFoundException$Message' => NULL, 'TestWindowSummary$Message' => 'If the test failed, the reason why it failed.
', 'WhatIfAnalysisSummary$Message' => 'If an error occurred, an informational message about the error.
', 'WhatIfForecastExportSummary$Message' => 'If an error occurred, an informational message about the error.
', 'WhatIfForecastSummary$Message' => 'If an error occurred, an informational message about the error.
', ], ], 'ErrorMetric' => [ 'base' => 'Provides detailed error metrics to evaluate the performance of a predictor. This object is part of the Metrics object.
', 'refs' => [ 'ErrorMetrics$member' => NULL, ], ], 'ErrorMetrics' => [ 'base' => NULL, 'refs' => [ 'Metrics$ErrorMetrics' => 'Provides detailed error metrics for each forecast type. Metrics include root-mean square-error (RMSE), mean absolute percentage error (MAPE), mean absolute scaled error (MASE), and weighted average percentage error (WAPE).
', ], ], 'EvaluationParameters' => [ 'base' => 'Parameters that define how to split a dataset into training data and testing data, and the number of iterations to perform. These parameters are specified in the predefined algorithms but you can override them in the CreatePredictor request.
', 'refs' => [ 'CreatePredictorRequest$EvaluationParameters' => 'Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.
', 'DescribePredictorResponse$EvaluationParameters' => 'Used to override the default evaluation parameters of the specified algorithm. Amazon Forecast evaluates a predictor by splitting a dataset into training data and testing data. The evaluation parameters define how to perform the split and the number of iterations.
', ], ], 'EvaluationResult' => [ 'base' => 'The results of evaluating an algorithm. Returned as part of the GetAccuracyMetrics response.
', 'refs' => [ 'PredictorEvaluationResults$member' => NULL, ], ], 'EvaluationState' => [ 'base' => NULL, 'refs' => [ 'DescribeMonitorResponse$LastEvaluationState' => 'The state of the monitor\'s latest evaluation.
', 'PredictorMonitorEvaluation$EvaluationState' => 'The status of the monitor evaluation. The state can be SUCCESS or FAILURE.
The type of evaluation.
SUMMARY - The average metrics across all windows.
COMPUTED - The metrics for the specified window.
An array of objects that summarize the properties of each Explainability resource.
', ], ], 'ExplainabilityConfig' => [ 'base' => 'The ExplainabilityConfig data type defines the number of time series and time points included in CreateExplainability.
If you provide a predictor ARN for ResourceArn, you must set both TimePointGranularity and TimeSeriesGranularity to “ALL”. When creating Predictor Explainability, Amazon Forecast considers all time series and time points.
If you provide a forecast ARN for ResourceArn, you can set TimePointGranularity and TimeSeriesGranularity to either “ALL” or “Specific”.
The configuration settings that define the granularity of time series and time points for the Explainability.
', 'DescribeExplainabilityResponse$ExplainabilityConfig' => 'The configuration settings that define the granularity of time series and time points for the Explainability.
', 'ExplainabilitySummary$ExplainabilityConfig' => 'The configuration settings that define the granularity of time series and time points for the Explainability.
', ], ], 'ExplainabilityExportSummary' => [ 'base' => 'Provides a summary of the Explainability export properties used in the ListExplainabilityExports operation. To get a complete set of properties, call the DescribeExplainabilityExport operation, and provide the ExplainabilityExportArn.
An array of objects that summarize the properties of each Explainability export.
', ], ], 'ExplainabilityInfo' => [ 'base' => 'Provides information about the Explainability resource.
', 'refs' => [ 'DescribeAutoPredictorResponse$ExplainabilityInfo' => 'Provides the status and ARN of the Predictor Explainability.
', ], ], 'ExplainabilitySummary' => [ 'base' => 'Provides a summary of the Explainability properties used in the ListExplainabilities operation. To get a complete set of properties, call the DescribeExplainability operation, and provide the listed ExplainabilityArn.
This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see AttributeConfig.
Provides featurization (transformation) information for a dataset field. This object is part of the FeaturizationConfig object.
For example:
{
"AttributeName": "demand",
FeaturizationPipeline [ {
"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "avg", "backfill": "nan"}
} ]
}
This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see AttributeConfig.
In a CreatePredictor operation, the specified algorithm trains a model using the specified dataset group. You can optionally tell the operation to modify data fields prior to training a model. These modifications are referred to as featurization.
You define featurization using the FeaturizationConfig object. You specify an array of transformations, one for each field that you want to featurize. You then include the FeaturizationConfig object in your CreatePredictor request. Amazon Forecast applies the featurization to the TARGET_TIME_SERIES and RELATED_TIME_SERIES datasets before model training.
You can create multiple featurization configurations. For example, you might call the CreatePredictor operation twice by specifying different featurization configurations.
The featurization configuration.
', 'DescribePredictorResponse$FeaturizationConfig' => 'The featurization configuration.
', ], ], 'FeaturizationMethod' => [ 'base' => 'Provides information about the method that featurizes (transforms) a dataset field. The method is part of the FeaturizationPipeline of the Featurization object.
The following is an example of how you specify a FeaturizationMethod object.
{
"FeaturizationMethodName": "filling",
"FeaturizationMethodParameters": {"aggregation": "sum", "middlefill": "zero", "backfill": "zero"}
}
The name of the method. The "filling" method is the only supported method.
', ], ], 'FeaturizationMethodParameters' => [ 'base' => NULL, 'refs' => [ 'FeaturizationMethod$FeaturizationMethodParameters' => 'The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.
The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Bold signifies the default value.
aggregation: sum, avg, first, min, max
frontfill: none
middlefill: zero, nan (not a number), value, median, mean, min, max
backfill: zero, nan, value, median, mean, min, max
The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):
middlefill: zero, value, median, mean, min, max
backfill: zero, value, median, mean, min, max
futurefill: zero, value, median, mean, min, max
To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".
An array of one FeaturizationMethod object that specifies the feature transformation method.
An array of featurization (transformation) information for the fields of a dataset.
', ], ], 'FieldStatistics' => [ 'base' => NULL, 'refs' => [ 'DescribeDatasetImportJobResponse$FieldStatistics' => 'Statistical information about each field in the input data.
', ], ], 'Filter' => [ 'base' => 'Describes a filter for choosing a subset of objects. Each filter consists of a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the objects that match the statement, respectively. The match statement consists of a key and a value.
The condition to apply. To include the objects that match the statement, specify IS. To exclude matching objects, specify IS_NOT.
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the datasets that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the datasets that match the statement, specify IS. To exclude matching datasets, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are DatasetArn and Status.
Value - The value to match.
For example, to list all dataset import jobs whose status is ACTIVE, you specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the resources that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT.
Key - The name of the parameter to filter on. Valid values are ResourceArn and Status.
Value - The value to match.
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude resources that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT.
Key - The name of the parameter to filter on. Valid values are ResourceArn and Status.
Value - The value to match.
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecast export jobs that match the statement, specify IS. To exclude matching forecast export jobs, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are ForecastArn and Status.
Value - The value to match.
For example, to list all jobs that export a forecast named electricityforecast, specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "ForecastArn", "Value": "arn:aws:forecast:us-west-2:<acct-id>:forecast/electricityforecast" } ]
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the forecasts that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecasts that match the statement, specify IS. To exclude matching forecasts, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are DatasetGroupArn, PredictorArn, and Status.
Value - The value to match.
For example, to list all forecasts whose status is not ACTIVE, you would specify:
"Filters": [ { "Condition": "IS_NOT", "Key": "Status", "Value": "ACTIVE" } ]
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the resources that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT.
Key - The name of the parameter to filter on. The only valid value is EvaluationState.
Value - The value to match. Valid values are only SUCCESS or FAILURE.
For example, to list only successful monitor evaluations, you would specify:
"Filters": [ { "Condition": "IS", "Key": "EvaluationState", "Value": "SUCCESS" } ]
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the resources that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT.
Key - The name of the parameter to filter on. The only valid value is Status.
Value - The value to match.
For example, to list all monitors who\'s status is ACTIVE, you would specify:
"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]
An array of filters. For each filter, provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the predictor backtest export jobs that match the statement from the list. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the predictor backtest export jobs that match the statement, specify IS. To exclude matching predictor backtest export jobs, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are PredictorArn and Status.
Value - The value to match.
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the predictors that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the predictors that match the statement, specify IS. To exclude matching predictors, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are DatasetGroupArn and Status.
Value - The value to match.
For example, to list all predictors whose status is ACTIVE, you would specify:
"Filters": [ { "Condition": "IS", "Key": "Status", "Value": "ACTIVE" } ]
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the what-if analysis jobs that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the what-if analysis jobs that match the statement, specify IS. To exclude matching what-if analysis jobs, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are WhatIfAnalysisArn and Status.
Value - The value to match.
For example, to list all jobs that export a forecast named electricityWhatIf, specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "WhatIfAnalysisArn", "Value": "arn:aws:forecast:us-west-2:<acct-id>:forecast/electricityWhatIf" } ]
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the what-if forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecast export jobs that match the statement, specify IS. To exclude matching forecast export jobs, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are WhatIfForecastExportArn and Status.
Value - The value to match.
For example, to list all jobs that export a forecast named electricityWIFExport, specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "WhatIfForecastExportArn", "Value": "arn:aws:forecast:us-west-2:<acct-id>:forecast/electricityWIFExport" } ]
An array of filters. For each filter, you provide a condition and a match statement. The condition is either IS or IS_NOT, which specifies whether to include or exclude the what-if forecast export jobs that match the statement from the list, respectively. The match statement consists of a key and a value.
Filter properties
Condition - The condition to apply. Valid values are IS and IS_NOT. To include the forecast export jobs that match the statement, specify IS. To exclude matching forecast export jobs, specify IS_NOT.
Key - The name of the parameter to filter on. Valid values are WhatIfForecastArn and Status.
Value - The value to match.
For example, to list all jobs that export a forecast named electricityWhatIfForecast, specify the following filter:
"Filters": [ { "Condition": "IS", "Key": "WhatIfForecastArn", "Value": "arn:aws:forecast:us-west-2:<acct-id>:forecast/electricityWhatIfForecast" } ]
An array of dimension (field) names that specify how to group the generated forecast.
For example, if you are generating forecasts for item sales across all your stores, and your dataset contains a store_id field, you would specify store_id as a dimension to group sales forecasts for each store.
An array of dimension (field) names that specify the attributes used to group your time series.
', 'FeaturizationConfig$ForecastDimensions' => 'An array of dimension (field) names that specify how to group the generated forecast.
For example, suppose that you are generating a forecast for item sales across all of your stores, and your dataset contains a store_id field. If you want the sales forecast for each item by store, you would specify store_id as the dimension.
All forecast dimensions specified in the TARGET_TIME_SERIES dataset don\'t need to be specified in the CreatePredictor request. All forecast dimensions specified in the RELATED_TIME_SERIES dataset must be specified in the CreatePredictor request.
Provides a summary of the forecast export job properties used in the ListForecastExportJobs operation. To get the complete set of properties, call the DescribeForecastExportJob operation, and provide the listed ForecastExportJobArn.
An array of objects that summarize each export job\'s properties.
', ], ], 'ForecastSummary' => [ 'base' => 'Provides a summary of the forecast properties used in the ListForecasts operation. To get the complete set of properties, call the DescribeForecast operation, and provide the ForecastArn that is listed in the summary.
The Forecast type used to compute WAPE, MAPE, MASE, and RMSE.
', 'ForecastTypes$member' => NULL, ], ], 'ForecastTypes' => [ 'base' => NULL, 'refs' => [ 'CreateAutoPredictorRequest$ForecastTypes' => 'The forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with mean.
The quantiles at which probabilistic forecasts are generated. You can currently specify up to 5 quantiles per forecast. Accepted values include 0.01 to 0.99 (increments of .01 only) and mean. The mean forecast is different from the median (0.50) when the distribution is not symmetric (for example, Beta and Negative Binomial).
The default quantiles are the quantiles you specified during predictor creation. If you didn\'t specify quantiles, the default values are ["0.1", "0.5", "0.9"].
Specifies the forecast types used to train a predictor. You can specify up to five forecast types. Forecast types can be quantiles from 0.01 to 0.99, by increments of 0.01 or higher. You can also specify the mean forecast with mean.
The default value is ["0.10", "0.50", "0.9"].
The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"].
', 'DescribeForecastResponse$ForecastTypes' => 'The quantiles at which probabilistic forecasts were generated.
', 'DescribePredictorResponse$ForecastTypes' => 'The forecast types used during predictor training. Default value is ["0.1","0.5","0.9"]
The quantiles at which probabilistic forecasts are generated. You can specify up to five quantiles per what-if forecast in the CreateWhatIfForecast operation. If you didn\'t specify quantiles, the default values are ["0.1", "0.5", "0.9"].
An array of objects that summarize each forecast\'s properties.
', ], ], 'Format' => [ 'base' => NULL, 'refs' => [ 'CreateDatasetImportJobRequest$Format' => 'The format of the imported data, CSV or PARQUET. The default value is CSV.
', 'CreateExplainabilityExportRequest$Format' => 'The format of the exported data, CSV or PARQUET.
', 'CreateForecastExportJobRequest$Format' => 'The format of the exported data, CSV or PARQUET. The default value is CSV.
', 'CreatePredictorBacktestExportJobRequest$Format' => 'The format of the exported data, CSV or PARQUET. The default value is CSV.
', 'CreateWhatIfForecastExportRequest$Format' => 'The format of the exported data, CSV or PARQUET.
', 'DescribeDatasetImportJobResponse$Format' => 'The format of the imported data, CSV or PARQUET.
', 'DescribeExplainabilityExportResponse$Format' => 'The format of the exported data, CSV or PARQUET.
', 'DescribeForecastExportJobResponse$Format' => 'The format of the exported data, CSV or PARQUET.
', 'DescribePredictorBacktestExportJobResponse$Format' => 'The format of the exported data, CSV or PARQUET.
', 'DescribeWhatIfForecastExportResponse$Format' => 'The format of the exported data, CSV or PARQUET.
', 'TimeSeriesIdentifiers$Format' => 'The format of the data, either CSV or PARQUET.
', 'TimeSeriesReplacementsDataSource$Format' => 'The format of the replacement data, CSV or PARQUET.
', ], ], 'Frequency' => [ 'base' => NULL, 'refs' => [ 'CreateAutoPredictorRequest$ForecastFrequency' => 'The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the RELATED_TIME_SERIES dataset frequency.
', 'CreateDatasetRequest$DataFrequency' => 'The frequency of data collection. This parameter is required for RELATED_TIME_SERIES datasets.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
', 'DescribeAutoPredictorResponse$ForecastFrequency' => 'The frequency of predictions in a forecast.
Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "Y" indicates every year and "5min" indicates every five minutes.
', 'DescribeDatasetResponse$DataFrequency' => 'The frequency of data collection.
Valid intervals are Y (Year), M (Month), W (Week), D (Day), H (Hour), 30min (30 minutes), 15min (15 minutes), 10min (10 minutes), 5min (5 minutes), and 1min (1 minute). For example, "M" indicates every month and "30min" indicates every 30 minutes.
', 'FeaturizationConfig$ForecastFrequency' => 'The frequency of predictions in a forecast.
Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, "1D" indicates every day and "15min" indicates every 15 minutes. You cannot specify a value that would overlap with the next larger frequency. That means, for example, you cannot specify a frequency of 60 minutes, because that is equivalent to 1 hour. The valid values for each frequency are the following:
Minute - 1-59
Hour - 1-23
Day - 1-6
Week - 1-4
Month - 1-11
Year - 1
Thus, if you want every other week forecasts, specify "2W". Or, if you want quarterly forecasts, you specify "3M".
The frequency must be greater than or equal to the TARGET_TIME_SERIES dataset frequency.
When a RELATED_TIME_SERIES dataset is provided, the frequency must be equal to the TARGET_TIME_SERIES dataset frequency.
', ], ], 'GeolocationFormat' => [ 'base' => NULL, 'refs' => [ 'CreateDatasetImportJobRequest$GeolocationFormat' => 'The format of the geolocation attribute. The geolocation attribute can be formatted in one of two ways:
LAT_LONG - the latitude and longitude in decimal format (Example: 47.61_-122.33).
CC_POSTALCODE (US Only) - the country code (US), followed by the 5-digit ZIP code (Example: US_98121).
The format of the geolocation attribute. Valid Values:"LAT_LONG" and "CC_POSTALCODE".
The hour of day to use for time alignment during aggregation.
', ], ], 'HyperParameterTuningJobConfig' => [ 'base' => 'Configuration information for a hyperparameter tuning job. You specify this object in the CreatePredictor request.
A hyperparameter is a parameter that governs the model training process. You set hyperparameters before training starts, unlike model parameters, which are determined during training. The values of the hyperparameters effect which values are chosen for the model parameters.
In a hyperparameter tuning job, Amazon Forecast chooses the set of hyperparameter values that optimize a specified metric. Forecast accomplishes this by running many training jobs over a range of hyperparameter values. The optimum set of values depends on the algorithm, the training data, and the specified metric objective.
', 'refs' => [ 'CreatePredictorRequest$HPOConfig' => 'Provides hyperparameter override values for the algorithm. If you don\'t provide this parameter, Amazon Forecast uses default values. The individual algorithms specify which hyperparameters support hyperparameter optimization (HPO). For more information, see aws-forecast-choosing-recipes.
If you included the HPOConfig object, you must set PerformHPO to true.
The hyperparameter override values for the algorithm.
', ], ], 'ImportMode' => [ 'base' => NULL, 'refs' => [ 'CreateDatasetImportJobRequest$ImportMode' => 'Specifies whether the dataset import job is a FULL or INCREMENTAL import. A FULL dataset import replaces all of the existing data with the newly imported data. An INCREMENTAL import appends the imported data to the existing data.
The import mode of the dataset import job, FULL or INCREMENTAL.
', 'DescribeDatasetImportJobResponse$ImportMode' => 'The import mode of the dataset import job, FULL or INCREMENTAL.
', ], ], 'InputDataConfig' => [ 'base' => 'This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see DataConfig.
The data used to train a predictor. The data includes a dataset group and any supplementary features. You specify this object in the CreatePredictor request.
', 'refs' => [ 'CreatePredictorRequest$InputDataConfig' => 'Describes the dataset group that contains the data to use to train the predictor.
', 'DescribePredictorResponse$InputDataConfig' => 'Describes the dataset group that contains the data to use to train the predictor.
', ], ], 'Integer' => [ 'base' => NULL, 'refs' => [ 'CreateAutoPredictorRequest$ForecastHorizon' => 'The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.
The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the TARGET_TIME_SERIES dataset length. If you are retraining an existing AutoPredictor, then the maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.
If you are upgrading to an AutoPredictor or retraining an existing AutoPredictor, you cannot update the forecast horizon parameter. You can meet this requirement by providing longer time-series in the dataset.
', 'CreatePredictorRequest$ForecastHorizon' => 'Specifies the number of time-steps that the model is trained to predict. The forecast horizon is also called the prediction length.
For example, if you configure a dataset for daily data collection (using the DataFrequency parameter of the CreateDataset operation) and set the forecast horizon to 10, the model returns predictions for 10 days.
The maximum forecast horizon is the lesser of 500 time-steps or 1/3 of the TARGET_TIME_SERIES dataset length.
', 'DescribeAutoPredictorResponse$ForecastHorizon' => 'The number of time-steps that the model predicts. The forecast horizon is also called the prediction length.
', 'DescribePredictorResponse$ForecastHorizon' => 'The number of time-steps of the forecast. The forecast horizon is also called the prediction length.
', 'EvaluationParameters$NumberOfBacktestWindows' => 'The number of times to split the input data. The default is 1. Valid values are 1 through 5.
', 'EvaluationParameters$BackTestWindowOffset' => 'The point from the end of the dataset where you want to split the data for model training and testing (evaluation). Specify the value as the number of data points. The default is the value of the forecast horizon. BackTestWindowOffset can be used to mimic a past virtual forecast start date. This value must be greater than or equal to the forecast horizon and less than half of the TARGET_TIME_SERIES dataset length.
ForecastHorizon <= BackTestWindowOffset < 1/2 * TARGET_TIME_SERIES dataset length
The maximum tunable value of the hyperparameter.
', 'IntegerParameterRange$MinValue' => 'The minimum tunable value of the hyperparameter.
', 'Statistics$Count' => 'The number of values in the field. If the response value is -1, refer to CountLong.
The number of distinct values in the field. If the response value is -1, refer to CountDistinctLong.
The number of null values in the field. If the response value is -1, refer to CountNullLong.
The number of NAN (not a number) values in the field. If the response value is -1, refer to CountNanLong.
The number of data points within the window.
', ], ], 'IntegerParameterRange' => [ 'base' => 'Specifies an integer hyperparameter and it\'s range of tunable values. This object is part of the ParameterRanges object.
', 'refs' => [ 'IntegerParameterRanges$member' => NULL, ], ], 'IntegerParameterRanges' => [ 'base' => NULL, 'refs' => [ 'ParameterRanges$IntegerParameterRanges' => 'Specifies the tunable range for each integer hyperparameter.
', ], ], 'InvalidInputException' => [ 'base' => 'We can\'t process the request because it includes an invalid value or a value that exceeds the valid range.
', 'refs' => [], ], 'InvalidNextTokenException' => [ 'base' => 'The token is not valid. Tokens expire after 24 hours.
', 'refs' => [], ], 'KMSKeyArn' => [ 'base' => NULL, 'refs' => [ 'EncryptionConfig$KMSKeyArn' => 'The Amazon Resource Name (ARN) of the KMS key.
', 'S3Config$KMSKeyArn' => 'The Amazon Resource Name (ARN) of an Key Management Service (KMS) key.
', ], ], 'LimitExceededException' => [ 'base' => 'The limit on the number of resources per account has been exceeded.
', 'refs' => [], ], 'ListDatasetGroupsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListDatasetGroupsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListDatasetImportJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListDatasetImportJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListDatasetsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListDatasetsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListExplainabilitiesRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListExplainabilitiesResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListExplainabilityExportsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListExplainabilityExportsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListForecastExportJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListForecastExportJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListForecastsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListForecastsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListMonitorEvaluationsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListMonitorEvaluationsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListMonitorsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListMonitorsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListPredictorBacktestExportJobsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListPredictorBacktestExportJobsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListPredictorsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListPredictorsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListTagsForResourceRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListTagsForResourceResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListWhatIfAnalysesRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListWhatIfAnalysesResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListWhatIfForecastExportsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListWhatIfForecastExportsResponse' => [ 'base' => NULL, 'refs' => [], ], 'ListWhatIfForecastsRequest' => [ 'base' => NULL, 'refs' => [], ], 'ListWhatIfForecastsResponse' => [ 'base' => NULL, 'refs' => [], ], 'LocalDateTime' => [ 'base' => NULL, 'refs' => [ 'CreateExplainabilityRequest$StartDateTime' => 'If TimePointGranularity is set to SPECIFIC, define the first point for the Explainability.
Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)
', 'CreateExplainabilityRequest$EndDateTime' => 'If TimePointGranularity is set to SPECIFIC, define the last time point for the Explainability.
Use the following timestamp format: yyyy-MM-ddTHH:mm:ss (example: 2015-01-01T20:00:00)
', 'DescribeExplainabilityResponse$StartDateTime' => 'If TimePointGranularity is set to SPECIFIC, the first time point in the Explainability.
If TimePointGranularity is set to SPECIFIC, the last time point in the Explainability.
The estimated time remaining in minutes for the predictor training job to complete.
', 'DescribeDatasetImportJobResponse$EstimatedTimeRemainingInMinutes' => 'The estimated time remaining in minutes for the dataset import job to complete.
', 'DescribeExplainabilityResponse$EstimatedTimeRemainingInMinutes' => 'The estimated time remaining in minutes for the CreateExplainability job to complete.
', 'DescribeForecastResponse$EstimatedTimeRemainingInMinutes' => 'The estimated time remaining in minutes for the forecast job to complete.
', 'DescribeMonitorResponse$EstimatedEvaluationTimeRemainingInMinutes' => 'The estimated number of minutes remaining before the monitor resource finishes its current evaluation.
', 'DescribePredictorResponse$EstimatedTimeRemainingInMinutes' => 'The estimated time remaining in minutes for the predictor training job to complete.
', 'DescribeWhatIfAnalysisResponse$EstimatedTimeRemainingInMinutes' => 'The approximate time remaining to complete the what-if analysis, in minutes.
', 'DescribeWhatIfForecastExportResponse$EstimatedTimeRemainingInMinutes' => 'The approximate time remaining to complete the what-if forecast export, in minutes.
', 'DescribeWhatIfForecastResponse$EstimatedTimeRemainingInMinutes' => 'The approximate time remaining to complete the what-if forecast, in minutes.
', 'PredictorMonitorEvaluation$NumItemsEvaluated' => 'The number of items considered during the evaluation.
', 'Statistics$CountLong' => 'The number of values in the field. CountLong is used instead of Count if the value is greater than 2,147,483,647.
The number of distinct values in the field. CountDistinctLong is used instead of CountDistinct if the value is greater than 2,147,483,647.
The number of null values in the field. CountNullLong is used instead of CountNull if the value is greater than 2,147,483,647.
The number of NAN (not a number) values in the field. CountNanLong is used instead of CountNan if the value is greater than 2,147,483,647.
The Amazon Resource Name (ARN) of the what-if forecast.
', 'CreateWhatIfForecastResponse$WhatIfForecastArn' => 'The Amazon Resource Name (ARN) of the what-if forecast.
', 'DeleteWhatIfForecastExportRequest$WhatIfForecastExportArn' => 'The Amazon Resource Name (ARN) of the what-if forecast export that you want to delete.
', 'DeleteWhatIfForecastRequest$WhatIfForecastArn' => 'The Amazon Resource Name (ARN) of the what-if forecast that you want to delete.
', 'DescribeWhatIfForecastExportRequest$WhatIfForecastExportArn' => 'The Amazon Resource Name (ARN) of the what-if forecast export that you are interested in.
', 'DescribeWhatIfForecastExportResponse$WhatIfForecastExportArn' => 'The Amazon Resource Name (ARN) of the what-if forecast export.
', 'DescribeWhatIfForecastRequest$WhatIfForecastArn' => 'The Amazon Resource Name (ARN) of the what-if forecast that you are interested in.
', 'DescribeWhatIfForecastResponse$WhatIfForecastArn' => 'The Amazon Resource Name (ARN) of the what-if forecast.
', 'LongArnList$member' => NULL, 'WhatIfForecastArnListForExport$member' => NULL, 'WhatIfForecastExportSummary$WhatIfForecastExportArn' => 'The Amazon Resource Name (ARN) of the what-if forecast export.
', 'WhatIfForecastSummary$WhatIfForecastArn' => 'The Amazon Resource Name (ARN) of the what-if forecast.
', ], ], 'LongArnList' => [ 'base' => NULL, 'refs' => [ 'DescribeWhatIfForecastExportResponse$WhatIfForecastArns' => 'An array of Amazon Resource Names (ARNs) that represent all of the what-if forecasts exported in this resource.
', ], ], 'MaxResults' => [ 'base' => NULL, 'refs' => [ 'ListDatasetGroupsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListDatasetImportJobsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListDatasetsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListExplainabilitiesRequest$MaxResults' => 'The number of items returned in the response.
', 'ListExplainabilityExportsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListForecastExportJobsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListForecastsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListMonitorEvaluationsRequest$MaxResults' => 'The maximum number of monitoring results to return.
', 'ListMonitorsRequest$MaxResults' => 'The maximum number of monitors to include in the response.
', 'ListPredictorBacktestExportJobsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListPredictorsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListWhatIfAnalysesRequest$MaxResults' => 'The number of items to return in the response.
', 'ListWhatIfForecastExportsRequest$MaxResults' => 'The number of items to return in the response.
', 'ListWhatIfForecastsRequest$MaxResults' => 'The number of items to return in the response.
', ], ], 'Message' => [ 'base' => NULL, 'refs' => [ 'DescribeAutoPredictorResponse$Message' => 'In the event of an error, a message detailing the cause of the error.
', 'DescribeDatasetImportJobResponse$Message' => 'If an error occurred, an informational message about the error.
', 'DescribeExplainabilityExportResponse$Message' => 'Information about any errors that occurred during the export.
', 'DescribeExplainabilityResponse$Message' => 'If an error occurred, a message about the error.
', 'DescribeForecastExportJobResponse$Message' => 'If an error occurred, an informational message about the error.
', 'DescribeMonitorResponse$Message' => 'An error message, if any, for the monitor.
', 'DescribePredictorBacktestExportJobResponse$Message' => 'Information about any errors that may have occurred during the backtest export.
', 'DescribePredictorResponse$Message' => 'If an error occurred, an informational message about the error.
', 'DescribeWhatIfForecastExportResponse$Message' => 'If an error occurred, an informational message about the error.
', 'ExplainabilitySummary$Message' => 'Information about any errors that may have occurred during the Explainability creation process.
', 'PredictorMonitorEvaluation$Message' => 'Information about any errors that may have occurred during the monitor evaluation.
', ], ], 'MetricName' => [ 'base' => NULL, 'refs' => [ 'MetricResult$MetricName' => 'The name of the metric.
', ], ], 'MetricResult' => [ 'base' => 'An individual metric Forecast calculated when monitoring predictor usage. You can compare the value for this metric to the metric\'s value in the Baseline to see how your predictor\'s performance is changing.
For more information about metrics generated by Forecast see Evaluating Predictor Accuracy
', 'refs' => [ 'MetricResults$member' => NULL, ], ], 'MetricResults' => [ 'base' => NULL, 'refs' => [ 'PredictorMonitorEvaluation$MetricResults' => 'A list of metrics Forecast calculated when monitoring a predictor. You can compare the value for each metric in the list to the metric\'s value in the Baseline to see how your predictor\'s performance is changing.
', ], ], 'Metrics' => [ 'base' => 'Provides metrics that are used to evaluate the performance of a predictor. This object is part of the WindowSummary object.
', 'refs' => [ 'WindowSummary$Metrics' => 'Provides metrics used to evaluate the performance of a predictor.
', ], ], 'MonitorConfig' => [ 'base' => 'The configuration details for the predictor monitor.
', 'refs' => [ 'CreateAutoPredictorRequest$MonitorConfig' => 'The configuration details for predictor monitoring. Provide a name for the monitor resource to enable predictor monitoring.
Predictor monitoring allows you to see how your predictor\'s performance changes over time. For more information, see Predictor Monitoring.
', ], ], 'MonitorDataSource' => [ 'base' => 'The source of the data the monitor used during the evaluation.
', 'refs' => [ 'PredictorMonitorEvaluation$MonitorDataSource' => 'The source of the data the monitor resource used during the evaluation.
', ], ], 'MonitorInfo' => [ 'base' => 'Provides information about the monitor resource.
', 'refs' => [ 'DescribeAutoPredictorResponse$MonitorInfo' => 'A object with the Amazon Resource Name (ARN) and status of the monitor resource.
', ], ], 'MonitorSummary' => [ 'base' => 'Provides a summary of the monitor properties used in the ListMonitors operation. To get a complete set of properties, call the DescribeMonitor operation, and provide the listed MonitorArn.
An array of objects that summarize each monitor\'s properties.
', ], ], 'Month' => [ 'base' => NULL, 'refs' => [ 'TimeAlignmentBoundary$Month' => 'The month to use for time alignment during aggregation. The month must be in uppercase.
', ], ], 'Name' => [ 'base' => NULL, 'refs' => [ 'Action$AttributeName' => 'The related time series that you are modifying. This value is case insensitive.
', 'AdditionalDataset$Name' => 'The name of the additional dataset. Valid names: "holiday" and "weather".
The name of the attribute as specified in the schema. Amazon Forecast supports the target field of the target time series and the related time series datasets. For example, for the RETAIL domain, the target is demand.
The name of the metric.
', 'CategoricalParameterRange$Name' => 'The name of the categorical hyperparameter to tune.
', 'Configuration$key' => NULL, 'ContinuousParameterRange$Name' => 'The name of the hyperparameter to tune.
', 'CreateAutoPredictorRequest$PredictorName' => 'A unique name for the predictor
', 'CreateDatasetGroupRequest$DatasetGroupName' => 'A name for the dataset group.
', 'CreateDatasetImportJobRequest$DatasetImportJobName' => 'The name for the dataset import job. We recommend including the current timestamp in the name, for example, 20190721DatasetImport. This can help you avoid getting a ResourceAlreadyExistsException exception.
A name for the dataset.
', 'CreateExplainabilityExportRequest$ExplainabilityExportName' => 'A unique name for the Explainability export.
', 'CreateExplainabilityRequest$ExplainabilityName' => 'A unique name for the Explainability.
', 'CreateForecastExportJobRequest$ForecastExportJobName' => 'The name for the forecast export job.
', 'CreateForecastRequest$ForecastName' => 'A name for the forecast.
', 'CreateMonitorRequest$MonitorName' => 'The name of the monitor resource.
', 'CreatePredictorBacktestExportJobRequest$PredictorBacktestExportJobName' => 'The name for the backtest export job.
', 'CreatePredictorRequest$PredictorName' => 'A name for the predictor.
', 'CreateWhatIfAnalysisRequest$WhatIfAnalysisName' => 'The name of the what-if analysis. Each name must be unique.
', 'CreateWhatIfForecastExportRequest$WhatIfForecastExportName' => 'The name of the what-if forecast to export.
', 'CreateWhatIfForecastRequest$WhatIfForecastName' => 'The name of the what-if forecast. Names must be unique within each what-if analysis.
', 'DatasetGroupSummary$DatasetGroupName' => 'The name of the dataset group.
', 'DatasetImportJobSummary$DatasetImportJobName' => 'The name of the dataset import job.
', 'DatasetSummary$DatasetName' => 'The name of the dataset.
', 'DescribeAutoPredictorResponse$PredictorName' => 'The name of the predictor.
', 'DescribeDatasetGroupResponse$DatasetGroupName' => 'The name of the dataset group.
', 'DescribeDatasetImportJobResponse$DatasetImportJobName' => 'The name of the dataset import job.
', 'DescribeDatasetResponse$DatasetName' => 'The name of the dataset.
', 'DescribeExplainabilityExportResponse$ExplainabilityExportName' => 'The name of the Explainability export.
', 'DescribeExplainabilityResponse$ExplainabilityName' => 'The name of the Explainability.
', 'DescribeForecastExportJobResponse$ForecastExportJobName' => 'The name of the forecast export job.
', 'DescribeForecastResponse$ForecastName' => 'The name of the forecast.
', 'DescribeMonitorResponse$MonitorName' => 'The name of the monitor.
', 'DescribePredictorBacktestExportJobResponse$PredictorBacktestExportJobName' => 'The name of the predictor backtest export job.
', 'DescribePredictorResponse$PredictorArn' => 'The ARN of the predictor.
', 'DescribePredictorResponse$PredictorName' => 'The name of the predictor.
', 'DescribeWhatIfAnalysisResponse$WhatIfAnalysisName' => 'The name of the what-if analysis.
', 'DescribeWhatIfForecastExportResponse$WhatIfForecastExportName' => 'The name of the what-if forecast export.
', 'DescribeWhatIfForecastResponse$WhatIfForecastName' => 'The name of the what-if forecast.
', 'ExplainabilityExportSummary$ExplainabilityExportName' => 'The name of the Explainability export
', 'ExplainabilitySummary$ExplainabilityName' => 'The name of the Explainability.
', 'Featurization$AttributeName' => 'The name of the schema attribute that specifies the data field to be featurized. Amazon Forecast supports the target field of the TARGET_TIME_SERIES and the RELATED_TIME_SERIES datasets. For example, for the RETAIL domain, the target is demand, and for the CUSTOM domain, the target is target_value. For more information, see howitworks-missing-values.
The name of the forecast export job.
', 'ForecastSummary$ForecastName' => 'The name of the forecast.
', 'IntegerParameterRange$Name' => 'The name of the hyperparameter to tune.
', 'MonitorConfig$MonitorName' => 'The name of the monitor resource.
', 'MonitorSummary$MonitorName' => 'The name of the monitor resource.
', 'PredictorBacktestExportJobSummary$PredictorBacktestExportJobName' => 'The name of the predictor backtest export job.
', 'PredictorSummary$PredictorName' => 'The name of the predictor.
', 'SchemaAttribute$AttributeName' => 'The name of the dataset field.
', 'SupplementaryFeature$Name' => 'The name of the feature. Valid values: "holiday" and "weather".
The item_id, dimension name, IM name, or timestamp that you are modifying.
', 'Transformations$key' => NULL, 'WhatIfAnalysisSummary$WhatIfAnalysisName' => 'The name of the what-if analysis.
', 'WhatIfForecastExportSummary$WhatIfForecastExportName' => 'The what-if forecast export name.
', 'WhatIfForecastSummary$WhatIfForecastName' => 'The name of the what-if forecast.
', ], ], 'NextToken' => [ 'base' => NULL, 'refs' => [ 'ListDatasetGroupsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListDatasetImportJobsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListDatasetsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListExplainabilitiesRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
', 'ListExplainabilitiesResponse$NextToken' => 'Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.
', 'ListExplainabilityExportsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
', 'ListExplainabilityExportsResponse$NextToken' => 'Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.
', 'ListForecastExportJobsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListForecastsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListMonitorEvaluationsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
', 'ListMonitorsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListPredictorBacktestExportJobsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
', 'ListPredictorBacktestExportJobsResponse$NextToken' => 'Returns this token if the response is truncated. To retrieve the next set of results, use the token in the next request.
', 'ListPredictorsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Amazon Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListWhatIfAnalysesRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request. Tokens expire after 24 hours.
If the response is truncated, Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListWhatIfForecastExportsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next
request. Tokens expire after 24 hours.
If the response is truncated, Forecast returns this token. To retrieve the next set of results, use the token in the next request.
', 'ListWhatIfForecastsRequest$NextToken' => 'If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next
request. Tokens expire after 24 hours.
If the result of the previous request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next
request. Tokens expire after 24 hours.
The operation that is applied to the provided attribute. Operations include:
ADD - adds Value to all rows of AttributeName.
SUBTRACT - subtracts Value from all rows of AttributeName.
MULTIPLY - multiplies all rows of AttributeName by Value.
DIVIDE - divides all rows of AttributeName by Value.
The accuracy metric used to optimize the predictor.
', 'CreatePredictorRequest$OptimizationMetric' => 'The accuracy metric used to optimize the predictor.
', 'DescribeAutoPredictorResponse$OptimizationMetric' => 'The accuracy metric used to optimize the predictor.
', 'DescribePredictorResponse$OptimizationMetric' => 'The accuracy metric used to optimize the predictor.
', 'GetAccuracyMetricsResponse$OptimizationMetric' => 'The accuracy metric used to optimize the predictor.
', ], ], 'ParameterKey' => [ 'base' => NULL, 'refs' => [ 'FeaturizationMethodParameters$key' => NULL, 'TrainingParameters$key' => NULL, ], ], 'ParameterRanges' => [ 'base' => 'Specifies the categorical, continuous, and integer hyperparameters, and their ranges of tunable values. The range of tunable values determines which values that a hyperparameter tuning job can choose for the specified hyperparameter. This object is part of the HyperParameterTuningJobConfig object.
', 'refs' => [ 'HyperParameterTuningJobConfig$ParameterRanges' => 'Specifies the ranges of valid values for the hyperparameters.
', ], ], 'ParameterValue' => [ 'base' => NULL, 'refs' => [ 'FeaturizationMethodParameters$value' => NULL, 'TrainingParameters$value' => NULL, ], ], 'PredictorBacktestExportJobSummary' => [ 'base' => 'Provides a summary of the predictor backtest export job properties used in the ListPredictorBacktestExportJobs operation. To get a complete set of properties, call the DescribePredictorBacktestExportJob operation, and provide the listed PredictorBacktestExportJobArn.
An array of objects that summarize the properties of each predictor backtest export job.
', ], ], 'PredictorBaseline' => [ 'base' => 'Metrics you can use as a baseline for comparison purposes. Use these metrics when you interpret monitoring results for an auto predictor.
', 'refs' => [ 'Baseline$PredictorBaseline' => 'The initial accuracy metrics for the predictor you are monitoring. Use these metrics as a baseline for comparison purposes as you use your predictor and the metrics change.
', ], ], 'PredictorEvaluationResults' => [ 'base' => NULL, 'refs' => [ 'GetAccuracyMetricsResponse$PredictorEvaluationResults' => 'An array of results from evaluating the predictor.
', ], ], 'PredictorEvent' => [ 'base' => 'Provides details about a predictor event, such as a retraining.
', 'refs' => [ 'PredictorMonitorEvaluation$PredictorEvent' => 'Provides details about a predictor event, such as a retraining.
', ], ], 'PredictorExecution' => [ 'base' => 'The algorithm used to perform a backtest and the status of those tests.
', 'refs' => [ 'PredictorExecutions$member' => NULL, ], ], 'PredictorExecutionDetails' => [ 'base' => 'Contains details on the backtests performed to evaluate the accuracy of the predictor. The tests are returned in descending order of accuracy, with the most accurate backtest appearing first. You specify the number of backtests to perform when you call the operation.
', 'refs' => [ 'DescribePredictorResponse$PredictorExecutionDetails' => 'Details on the the status and results of the backtests performed to evaluate the accuracy of the predictor. You specify the number of backtests to perform when you call the operation.
', ], ], 'PredictorExecutions' => [ 'base' => NULL, 'refs' => [ 'PredictorExecutionDetails$PredictorExecutions' => 'An array of the backtests performed to evaluate the accuracy of the predictor against a particular algorithm. The NumberOfBacktestWindows from the object determines the number of windows in the array.
Describes the results of a monitor evaluation.
', 'refs' => [ 'PredictorMonitorEvaluations$member' => NULL, ], ], 'PredictorMonitorEvaluations' => [ 'base' => NULL, 'refs' => [ 'ListMonitorEvaluationsResponse$PredictorMonitorEvaluations' => 'The monitoring results and predictor events collected by the monitor resource during different windows of time.
For information about monitoring see Viewing Monitoring Results. For more information about retrieving monitoring results see Viewing Monitoring Results.
', ], ], 'PredictorSummary' => [ 'base' => 'Provides a summary of the predictor properties that are used in the ListPredictors operation. To get the complete set of properties, call the DescribePredictor operation, and provide the listed PredictorArn.
An array of objects that summarize each predictor\'s properties.
', ], ], 'ReferencePredictorSummary' => [ 'base' => 'Provides a summary of the reference predictor used when retraining or upgrading a predictor.
', 'refs' => [ 'DescribeAutoPredictorResponse$ReferencePredictorSummary' => 'The ARN and state of the reference predictor. This parameter is only valid for retrained or upgraded predictors.
', 'PredictorSummary$ReferencePredictorSummary' => 'A summary of the reference predictor used if the predictor was retrained or upgraded.
', ], ], 'ResourceAlreadyExistsException' => [ 'base' => 'There is already a resource with this name. Try again with a different name.
', 'refs' => [], ], 'ResourceInUseException' => [ 'base' => 'The specified resource is in use.
', 'refs' => [], ], 'ResourceNotFoundException' => [ 'base' => 'We can\'t find a resource with that Amazon Resource Name (ARN). Check the ARN and try again.
', 'refs' => [], ], 'ResumeResourceRequest' => [ 'base' => NULL, 'refs' => [], ], 'S3Config' => [ 'base' => 'The path to the file(s) in an Amazon Simple Storage Service (Amazon S3) bucket, and an Identity and Access Management (IAM) role that Amazon Forecast can assume to access the file(s). Optionally, includes an Key Management Service (KMS) key. This object is part of the DataSource object that is submitted in the CreateDatasetImportJob request, and part of the DataDestination object.
', 'refs' => [ 'DataDestination$S3Config' => 'The path to an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the bucket.
', 'DataSource$S3Config' => 'The path to the data stored in an Amazon Simple Storage Service (Amazon S3) bucket along with the credentials to access the data.
', 'TimeSeriesReplacementsDataSource$S3Config' => NULL, ], ], 'S3Path' => [ 'base' => NULL, 'refs' => [ 'S3Config$Path' => 'The path to an Amazon Simple Storage Service (Amazon S3) bucket or file(s) in an Amazon S3 bucket.
', ], ], 'ScalingType' => [ 'base' => NULL, 'refs' => [ 'ContinuousParameterRange$ScalingType' => 'The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values:
Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter.
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have values greater than 0.
hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0.
For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
', 'IntegerParameterRange$ScalingType' => 'The scale that hyperparameter tuning uses to search the hyperparameter range. Valid values:
Amazon Forecast hyperparameter tuning chooses the best scale for the hyperparameter.
Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.
Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale.
Logarithmic scaling works only for ranges that have values greater than 0.
Not supported for IntegerParameterRange.
Reverse logarithmic scaling works only for ranges that are entirely within the range 0 <= x < 1.0.
For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:
', ], ], 'Schema' => [ 'base' => 'Defines the fields of a dataset.
', 'refs' => [ 'CreateDatasetRequest$Schema' => 'The schema for the dataset. The schema attributes and their order must match the fields in your data. The dataset Domain and DatasetType that you choose determine the minimum required fields in your training data. For information about the required fields for a specific dataset domain and type, see Dataset Domains and Dataset Types.
An array of SchemaAttribute objects that specify the dataset fields. Each SchemaAttribute specifies the name and data type of a field.
An attribute of a schema, which defines a dataset field. A schema attribute is required for every field in a dataset. The Schema object contains an array of SchemaAttribute objects.
An array of attributes specifying the name and type of each field in a dataset.
', ], ], 'State' => [ 'base' => NULL, 'refs' => [ 'ReferencePredictorSummary$State' => 'Whether the reference predictor is Active or Deleted.
Provides statistics for each data field imported into to an Amazon Forecast dataset with the CreateDatasetImportJob operation.
', 'refs' => [ 'FieldStatistics$value' => NULL, ], ], 'Status' => [ 'base' => NULL, 'refs' => [ 'DatasetImportJobSummary$Status' => 'The status of the dataset import job. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
CREATE_STOPPING, CREATE_STOPPED
The status of the predictor. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the dataset group. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
The UPDATE states apply when you call the UpdateDatasetGroup operation.
The Status of the dataset group must be ACTIVE before you can use the dataset group to create a predictor.
The status of the dataset import job. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
CREATE_STOPPING, CREATE_STOPPED
The status of the dataset. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
UPDATE_PENDING, UPDATE_IN_PROGRESS, UPDATE_FAILED
The UPDATE states apply while data is imported to the dataset from a call to the CreateDatasetImportJob operation and reflect the status of the dataset import job. For example, when the import job status is CREATE_IN_PROGRESS, the status of the dataset is UPDATE_IN_PROGRESS.
The Status of the dataset must be ACTIVE before you can import training data.
The status of the Explainability export. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the Explainability resource. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the forecast export job. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the forecast export job must be ACTIVE before you can access the forecast in your S3 bucket.
The status of the monitor resource.
', 'DescribePredictorBacktestExportJobResponse$Status' => 'The status of the predictor backtest export job. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the predictor. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
CREATE_STOPPING, CREATE_STOPPED
The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.
The status of the what-if forecast. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the what-if forecast export must be ACTIVE before you can access the forecast export.
The status of the Explainability export. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the Explainability. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the Explainability. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the forecast export job. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the forecast export job must be ACTIVE before you can access the forecast in your S3 bucket.
The status of the forecast. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the forecast must be ACTIVE before you can query or export the forecast.
The status of the monitor. States include:
ACTIVE
ACTIVE_STOPPING, ACTIVE_STOPPED
UPDATE_IN_PROGRESS
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the monitor. States include:
ACTIVE
ACTIVE_STOPPING, ACTIVE_STOPPED
UPDATE_IN_PROGRESS
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the predictor backtest export job. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The status of the predictor. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
CREATE_STOPPING, CREATE_STOPPED
The Status of the predictor must be ACTIVE before you can use the predictor to create a forecast.
The status of the test. Possible status values are:
ACTIVE
CREATE_IN_PROGRESS
CREATE_FAILED
The status of the what-if analysis. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the what-if analysis must be ACTIVE before you can access the analysis.
The status of the what-if forecast export. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the what-if analysis must be ACTIVE before you can access the analysis.
The status of the what-if forecast. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the what-if analysis must be ACTIVE before you can access the analysis.
The status of the forecast. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the forecast must be ACTIVE before you can query or export the forecast.
The status of the what-if analysis. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the what-if analysis must be ACTIVE before you can access the analysis.
The status of the what-if forecast. States include:
ACTIVE
CREATE_PENDING, CREATE_IN_PROGRESS, CREATE_FAILED
CREATE_STOPPING, CREATE_STOPPED
DELETE_PENDING, DELETE_IN_PROGRESS, DELETE_FAILED
The Status of the what-if forecast must be ACTIVE before you can access the forecast.
The name of the parameter to filter on.
', 'ForecastSummary$PredictorArn' => 'The ARN of the predictor used to generate the forecast.
', 'ForecastSummary$DatasetGroupArn' => 'The Amazon Resource Name (ARN) of the dataset group that provided the data used to train the predictor.
', 'Statistics$Min' => 'For a numeric field, the minimum value in the field.
', 'Statistics$Max' => 'For a numeric field, the maximum value in the field.
', ], ], 'SupplementaryFeature' => [ 'base' => 'This object belongs to the CreatePredictor operation. If you created your predictor with CreateAutoPredictor, see AdditionalDataset.
Describes a supplementary feature of a dataset group. This object is part of the InputDataConfig object. Forecast supports the Weather Index and Holidays built-in featurizations.
Weather Index
The Amazon Forecast Weather Index is a built-in featurization that incorporates historical and projected weather information into your model. The Weather Index supplements your datasets with over two years of historical weather data and up to 14 days of projected weather data. For more information, see Amazon Forecast Weather Index.
Holidays
Holidays is a built-in featurization that incorporates a feature-engineered dataset of national holiday information into your model. It provides native support for the holiday calendars of 66 countries. To view the holiday calendars, refer to the Jollyday library. For more information, see Holidays Featurization.
', 'refs' => [ 'SupplementaryFeatures$member' => NULL, ], ], 'SupplementaryFeatures' => [ 'base' => NULL, 'refs' => [ 'InputDataConfig$SupplementaryFeatures' => 'An array of supplementary features. The only supported feature is a holiday calendar.
', ], ], 'Tag' => [ 'base' => 'The optional metadata that you apply to a resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The keys of the tags to be removed.
', ], ], 'TagResourceRequest' => [ 'base' => NULL, 'refs' => [], ], 'TagResourceResponse' => [ 'base' => NULL, 'refs' => [], ], 'TagValue' => [ 'base' => NULL, 'refs' => [ 'Tag$Value' => 'The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
Optional metadata to help you categorize and organize your predictors. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this prefix.
The optional metadata that you apply to the dataset group to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
The optional metadata that you apply to the dataset import job to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
The optional metadata that you apply to the dataset to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
Optional metadata to help you categorize and organize your resources. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this prefix.
Optional metadata to help you categorize and organize your resources. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this prefix.
The optional metadata that you apply to the forecast export job to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
The optional metadata that you apply to the forecast to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
A list of tags to apply to the monitor resource.
', 'CreatePredictorBacktestExportJobRequest$Tags' => 'Optional metadata to help you categorize and organize your backtests. Each tag consists of a key and an optional value, both of which you define. Tag keys and values are case sensitive.
The following restrictions apply to tags:
For each resource, each tag key must be unique and each tag key must have one value.
Maximum number of tags per resource: 50.
Maximum key length: 128 Unicode characters in UTF-8.
Maximum value length: 256 Unicode characters in UTF-8.
Accepted characters: all letters and numbers, spaces representable in UTF-8, and + - = . _ : / @. If your tagging schema is used across other services and resources, the character restrictions of those services also apply.
Key prefixes cannot include any upper or lowercase combination of aws: or AWS:. Values can have this prefix. If a tag value has aws as its prefix but the key does not, Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit. You cannot edit or delete tag keys with this prefix.
The optional metadata that you apply to the predictor to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
A list of tags to apply to the what if forecast.
', 'CreateWhatIfForecastExportRequest$Tags' => 'A list of tags to apply to the what if forecast.
', 'CreateWhatIfForecastRequest$Tags' => 'A list of tags to apply to the what if forecast.
', 'ListTagsForResourceResponse$Tags' => 'The tags for the resource.
', 'TagResourceRequest$Tags' => 'The tags to add to the resource. A tag is an array of key-value pairs.
The following basic restrictions apply to tags:
Maximum number of tags per resource - 50.
For each resource, each tag key must be unique, and each tag key can have only one value.
Maximum key length - 128 Unicode characters in UTF-8.
Maximum value length - 256 Unicode characters in UTF-8.
If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.
Tag keys and values are case sensitive.
Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for Amazon Web Services use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Forecast considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.
An array of test windows used to evaluate the algorithm. The NumberOfBacktestWindows from the object determines the number of windows in the array.
The status, start time, and end time of a backtest, as well as a failure reason if applicable.
', 'refs' => [ 'TestWindowDetails$member' => NULL, ], ], 'TestWindows' => [ 'base' => NULL, 'refs' => [ 'EvaluationResult$TestWindows' => 'The array of test windows used for evaluating the algorithm. The NumberOfBacktestWindows from the EvaluationParameters object determines the number of windows in the array.
The time boundary Forecast uses to align and aggregate your data to match your forecast frequency. Provide the unit of time and the time boundary as a key value pair. If you don\'t provide a time boundary, Forecast uses a set of Default Time Boundaries.
For more information about aggregation, see Data Aggregation for Different Forecast Frequencies. For more information setting a custom time boundary, see Specifying a Time Boundary.
', 'refs' => [ 'CreateAutoPredictorRequest$TimeAlignmentBoundary' => 'The time boundary Forecast uses to align and aggregate any data that doesn\'t align with your forecast frequency. Provide the unit of time and the time boundary as a key value pair. For more information on specifying a time boundary, see Specifying a Time Boundary. If you don\'t provide a time boundary, Forecast uses a set of Default Time Boundaries.
', 'DescribeAutoPredictorResponse$TimeAlignmentBoundary' => 'The time boundary Forecast uses when aggregating data.
', ], ], 'TimePointGranularity' => [ 'base' => NULL, 'refs' => [ 'ExplainabilityConfig$TimePointGranularity' => 'To create an Explainability for all time points in your forecast horizon, use ALL. To create an Explainability for specific time points in your forecast horizon, use SPECIFIC.
Specify time points with the StartDateTime and EndDateTime parameters within the CreateExplainability operation.
Creates a subset of items within an attribute that are modified. For example, you can use this operation to create a subset of items that cost $5 or less. To do this, you specify "AttributeName": "price", "AttributeValue": "5", and "Condition": "LESS_THAN". Pair this operation with the Action operation within the CreateWhatIfForecastRequest$TimeSeriesTransformations operation to define how the attribute is modified.
An array of conditions that define which members of the related time series are transformed.
', ], ], 'TimeSeriesGranularity' => [ 'base' => NULL, 'refs' => [ 'ExplainabilityConfig$TimeSeriesGranularity' => 'To create an Explainability for all time series in your datasets, use ALL. To create an Explainability for specific time series in your datasets, use SPECIFIC.
Specify time series by uploading a CSV or Parquet file to an Amazon S3 bucket and set the location within the DataDestination data type.
', ], ], 'TimeSeriesIdentifiers' => [ 'base' => 'Details about the import file that contains the time series for which you want to create forecasts.
', 'refs' => [ 'TimeSeriesSelector$TimeSeriesIdentifiers' => 'Details about the import file that contains the time series for which you want to create forecasts.
', ], ], 'TimeSeriesReplacementsDataSource' => [ 'base' => 'A replacement dataset is a modified version of the baseline related time series that contains only the values that you want to include in a what-if forecast. The replacement dataset must contain the forecast dimensions and item identifiers in the baseline related time series as well as at least 1 changed time series. This dataset is merged with the baseline related time series to create a transformed dataset that is used for the what-if forecast.
', 'refs' => [ 'CreateWhatIfForecastRequest$TimeSeriesReplacementsDataSource' => 'The replacement time series dataset, which contains the rows that you want to change in the related time series dataset. A replacement time series does not need to contain all rows that are in the baseline related time series. Include only the rows (measure-dimension combinations) that you want to include in the what-if forecast.
This dataset is merged with the original time series to create a transformed dataset that is used for the what-if analysis.
This dataset should contain the items to modify (such as item_id or workforce_type), any relevant dimensions, the timestamp column, and at least one of the related time series columns. This file should not contain duplicate timestamps for the same time series.
Timestamps and item_ids not included in this dataset are not included in the what-if analysis.
', 'DescribeWhatIfForecastResponse$TimeSeriesReplacementsDataSource' => 'An array of S3Config, Schema, and Format elements that describe the replacement time series.
Defines the set of time series that are used to create the forecasts in a TimeSeriesIdentifiers object.
The TimeSeriesIdentifiers object needs the following information:
DataSource
Format
Schema
Defines the set of time series that are used to create the forecasts in a TimeSeriesIdentifiers object.
The TimeSeriesIdentifiers object needs the following information:
DataSource
Format
Schema
Defines the set of time series that are used in the what-if analysis with a TimeSeriesIdentifiers object. What-if analyses are performed only for the time series in this object.
The TimeSeriesIdentifiers object needs the following information:
DataSource
Format
Schema
The time series to include in the forecast.
', 'DescribeWhatIfAnalysisResponse$TimeSeriesSelector' => NULL, ], ], 'TimeSeriesTransformation' => [ 'base' => 'A transformation function is a pair of operations that select and modify the rows in a related time series. You select the rows that you want with a condition operation and you modify the rows with a transformation operation. All conditions are joined with an AND operation, meaning that all conditions must be true for the transformation to be applied. Transformations are applied in the order that they are listed.
', 'refs' => [ 'TimeSeriesTransformations$member' => NULL, ], ], 'TimeSeriesTransformations' => [ 'base' => NULL, 'refs' => [ 'CreateWhatIfForecastRequest$TimeSeriesTransformations' => 'The transformations that are applied to the baseline time series. Each transformation contains an action and a set of conditions. An action is applied only when all conditions are met. If no conditions are provided, the action is applied to all items.
', 'DescribeWhatIfForecastResponse$TimeSeriesTransformations' => 'An array of Action and TimeSeriesConditions elements that describe what transformations were applied to which time series.
A single time zone for every item in your dataset. This option is ideal for datasets with all timestamps within a single time zone, or if all timestamps are normalized to a single time zone.
Refer to the Joda-Time API for a complete list of valid time zone names.
', 'DescribeDatasetImportJobResponse$TimeZone' => 'The single time zone applied to every item in the dataset
', ], ], 'Timestamp' => [ 'base' => NULL, 'refs' => [ 'DatasetGroupSummary$CreationTime' => 'When the dataset group was created.
', 'DatasetGroupSummary$LastModificationTime' => 'When the dataset group was created or last updated from a call to the UpdateDatasetGroup operation. While the dataset group is being updated, LastModificationTime is the current time of the ListDatasetGroups call.
When the dataset import job was created.
', 'DatasetImportJobSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the dataset was created.
', 'DatasetSummary$LastModificationTime' => 'When you create a dataset, LastModificationTime is the same as CreationTime. While data is being imported to the dataset, LastModificationTime is the current time of the ListDatasets call. After a CreateDatasetImportJob operation has finished, LastModificationTime is when the import job completed or failed.
The timestamp of the CreateAutoPredictor request.
', 'DescribeAutoPredictorResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the dataset group was created.
', 'DescribeDatasetGroupResponse$LastModificationTime' => 'When the dataset group was created or last updated from a call to the UpdateDatasetGroup operation. While the dataset group is being updated, LastModificationTime is the current time of the DescribeDatasetGroup call.
When the dataset import job was created.
', 'DescribeDatasetImportJobResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the dataset was created.
', 'DescribeDatasetResponse$LastModificationTime' => 'When you create a dataset, LastModificationTime is the same as CreationTime. While data is being imported to the dataset, LastModificationTime is the current time of the DescribeDataset call. After a CreateDatasetImportJob operation has finished, LastModificationTime is when the import job completed or failed.
When the Explainability export was created.
', 'DescribeExplainabilityExportResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the Explainability resource was created.
', 'DescribeExplainabilityResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the forecast export job was created.
', 'DescribeForecastExportJobResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the forecast creation task was created.
', 'DescribeForecastResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
The timestamp of the latest evaluation completed by the monitor.
', 'DescribeMonitorResponse$CreationTime' => 'The timestamp for when the monitor resource was created.
', 'DescribeMonitorResponse$LastModificationTime' => 'The timestamp of the latest modification to the monitor.
', 'DescribePredictorBacktestExportJobResponse$CreationTime' => 'When the predictor backtest export job was created.
', 'DescribePredictorBacktestExportJobResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the model training task was created.
', 'DescribePredictorResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the what-if analysis was created.
', 'DescribeWhatIfAnalysisResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the what-if forecast export was created.
', 'DescribeWhatIfForecastExportResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the what-if forecast was created.
', 'DescribeWhatIfForecastResponse$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the Explainability was created.
', 'ExplainabilityExportSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the Explainability was created.
', 'ExplainabilitySummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the forecast export job was created.
', 'ForecastExportJobSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the forecast creation task was created.
', 'ForecastSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the monitor resource was created.
', 'MonitorSummary$LastModificationTime' => 'The last time the monitor resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
STOPPED - When the resource stopped.
ACTIVE or CREATE_FAILED - When the monitor creation finished or failed.
When the predictor backtest export job was created.
', 'PredictorBacktestExportJobSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
The timestamp for when the event occurred.
', 'PredictorMonitorEvaluation$EvaluationTime' => 'The timestamp that indicates when the monitor evaluation was started.
', 'PredictorMonitorEvaluation$WindowStartDatetime' => 'The timestamp that indicates the start of the window that is used for monitor evaluation.
', 'PredictorMonitorEvaluation$WindowEndDatetime' => 'The timestamp that indicates the end of the window that is used for monitor evaluation.
', 'PredictorSummary$CreationTime' => 'When the model training task was created.
', 'PredictorSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
The time at which the test began.
', 'TestWindowSummary$TestWindowEnd' => 'The time at which the test ended.
', 'WhatIfAnalysisSummary$CreationTime' => 'When the what-if analysis was created.
', 'WhatIfAnalysisSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the what-if forecast export was created.
', 'WhatIfForecastExportSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
When the what-if forecast was created.
', 'WhatIfForecastSummary$LastModificationTime' => 'The last time the resource was modified. The timestamp depends on the status of the job:
CREATE_PENDING - The CreationTime.
CREATE_IN_PROGRESS - The current timestamp.
CREATE_STOPPING - The current timestamp.
CREATE_STOPPED - When the job stopped.
ACTIVE or CREATE_FAILED - When the job finished or failed.
The timestamp that defines the start of the window.
', 'WindowSummary$TestWindowEnd' => 'The timestamp that defines the end of the window.
', ], ], 'TimestampFormat' => [ 'base' => NULL, 'refs' => [ 'CreateDatasetImportJobRequest$TimestampFormat' => 'The format of timestamps in the dataset. The format that you specify depends on the DataFrequency specified when the dataset was created. The following formats are supported
"yyyy-MM-dd"
For the following data frequencies: Y, M, W, and D
"yyyy-MM-dd HH:mm:ss"
For the following data frequencies: H, 30min, 15min, and 1min; and optionally, for: Y, M, W, and D
If the format isn\'t specified, Amazon Forecast expects the format to be "yyyy-MM-dd HH:mm:ss".
', 'DescribeDatasetImportJobResponse$TimestampFormat' => 'The format of timestamps in the dataset. The format that you specify depends on the DataFrequency specified when the dataset was created. The following formats are supported
"yyyy-MM-dd"
For the following data frequencies: Y, M, W, and D
"yyyy-MM-dd HH:mm:ss"
For the following data frequencies: H, 30min, 15min, and 1min; and optionally, for: Y, M, W, and D
The timestamp format of the replacement data.
', ], ], 'TrainingParameters' => [ 'base' => NULL, 'refs' => [ 'CreatePredictorRequest$TrainingParameters' => 'The hyperparameters to override for model training. The hyperparameters that you can override are listed in the individual algorithms. For the list of supported algorithms, see aws-forecast-choosing-recipes.
', 'DescribePredictorResponse$TrainingParameters' => 'The default training parameters or overrides selected during model training. When running AutoML or choosing HPO with CNN-QR or DeepAR+, the optimized values for the chosen hyperparameters are returned. For more information, see aws-forecast-choosing-recipes.
', ], ], 'Transformations' => [ 'base' => NULL, 'refs' => [ 'AttributeConfig$Transformations' => 'The method parameters (key-value pairs), which are a map of override parameters. Specify these parameters to override the default values. Related Time Series attributes do not accept aggregation parameters.
The following list shows the parameters and their valid values for the "filling" featurization method for a Target Time Series dataset. Default values are bolded.
aggregation: sum, avg, first, min, max
frontfill: none
middlefill: zero, nan (not a number), value, median, mean, min, max
backfill: zero, nan, value, median, mean, min, max
The following list shows the parameters and their valid values for a Related Time Series featurization method (there are no defaults):
middlefill: zero, value, median, mean, min, max
backfill: zero, value, median, mean, min, max
futurefill: zero, value, median, mean, min, max
To set a filling method to a specific value, set the fill parameter to value and define the value in a corresponding _value parameter. For example, to set backfilling to a value of 2, include the following: "backfill": "value" and "backfill_value":"2".
Automatically derive time zone information from the geolocation attribute. This option is ideal for datasets that contain timestamps in multiple time zones and those timestamps are expressed in local time.
', 'DescribeDatasetImportJobResponse$UseGeolocationForTimeZone' => 'Whether TimeZone is automatically derived from the geolocation attribute.
Weather Index
To enable the Weather Index, set the value to "true"
Holidays
To enable Holidays, specify a country with one of the following two-letter country codes:
"AL" - ALBANIA
"AR" - ARGENTINA
"AT" - AUSTRIA
"AU" - AUSTRALIA
"BA" - BOSNIA HERZEGOVINA
"BE" - BELGIUM
"BG" - BULGARIA
"BO" - BOLIVIA
"BR" - BRAZIL
"BY" - BELARUS
"CA" - CANADA
"CL" - CHILE
"CO" - COLOMBIA
"CR" - COSTA RICA
"HR" - CROATIA
"CZ" - CZECH REPUBLIC
"DK" - DENMARK
"EC" - ECUADOR
"EE" - ESTONIA
"ET" - ETHIOPIA
"FI" - FINLAND
"FR" - FRANCE
"DE" - GERMANY
"GR" - GREECE
"HU" - HUNGARY
"IS" - ICELAND
"IN" - INDIA
"IE" - IRELAND
"IT" - ITALY
"JP" - JAPAN
"KZ" - KAZAKHSTAN
"KR" - KOREA
"LV" - LATVIA
"LI" - LIECHTENSTEIN
"LT" - LITHUANIA
"LU" - LUXEMBOURG
"MK" - MACEDONIA
"MT" - MALTA
"MX" - MEXICO
"MD" - MOLDOVA
"ME" - MONTENEGRO
"NL" - NETHERLANDS
"NZ" - NEW ZEALAND
"NI" - NICARAGUA
"NG" - NIGERIA
"NO" - NORWAY
"PA" - PANAMA
"PY" - PARAGUAY
"PE" - PERU
"PL" - POLAND
"PT" - PORTUGAL
"RO" - ROMANIA
"RU" - RUSSIA
"RS" - SERBIA
"SK" - SLOVAKIA
"SI" - SLOVENIA
"ZA" - SOUTH AFRICA
"ES" - SPAIN
"SE" - SWEDEN
"CH" - SWITZERLAND
"UA" - UKRAINE
"AE" - UNITED ARAB EMIRATES
"US" - UNITED STATES
"UK" - UNITED KINGDOM
"UY" - URUGUAY
"VE" - VENEZUELA
A list of the tunable categories for the hyperparameter.
', 'Configuration$value' => NULL, ], ], 'WeightedQuantileLoss' => [ 'base' => 'The weighted loss value for a quantile. This object is part of the Metrics object.
', 'refs' => [ 'WeightedQuantileLosses$member' => NULL, ], ], 'WeightedQuantileLosses' => [ 'base' => NULL, 'refs' => [ 'Metrics$WeightedQuantileLosses' => 'An array of weighted quantile losses. Quantiles divide a probability distribution into regions of equal probability. The distribution in this case is the loss function.
', ], ], 'WhatIfAnalyses' => [ 'base' => NULL, 'refs' => [ 'ListWhatIfAnalysesResponse$WhatIfAnalyses' => 'An array of WhatIfAnalysisSummary objects that describe the matched analyses.
Provides a summary of the what-if analysis properties used in the ListWhatIfAnalyses operation. To get the complete set of properties, call the DescribeWhatIfAnalysis operation, and provide the WhatIfAnalysisArn that is listed in the summary.
The list of what-if forecast Amazon Resource Names (ARNs) to export.
', 'WhatIfForecastExportSummary$WhatIfForecastArns' => 'An array of Amazon Resource Names (ARNs) that define the what-if forecasts included in the export.
', ], ], 'WhatIfForecastExportSummary' => [ 'base' => 'Provides a summary of the what-if forecast export properties used in the ListWhatIfForecastExports operation. To get the complete set of properties, call the DescribeWhatIfForecastExport operation, and provide the WhatIfForecastExportArn that is listed in the summary.
An array of WhatIfForecastExports objects that describe the matched forecast exports.
Provides a summary of the what-if forecast properties used in the ListWhatIfForecasts operation. To get the complete set of properties, call the DescribeWhatIfForecast operation, and provide the WhatIfForecastArn that is listed in the summary.
An array of WhatIfForecasts objects that describe the matched forecasts.
The metrics for a time range within the evaluation portion of a dataset. This object is part of the EvaluationResult object.
The TestWindowStart and TestWindowEnd parameters are determined by the BackTestWindowOffset parameter of the EvaluationParameters object.