Welcome to TwinStat’s documentation!

Indices and tables

Browse the API:

Or search for something specific:

A data science python library (TwinStat) is needed as a toolbox to be used by a variety of other AWS services ranging from Twinmaker, do-pm, SimSpace Weaver, SageMaker, etc. TwinStat was designed as a toolbox for the TwinFlow Level 4 Digital Twin Framework. The leveling framework can be read about here: https://aws.amazon.com/blogs/iot/digital-twins-on-aws-unlocking-business-value-and-outcomes/

Features

TwinStat includes the following methods:

  • Bayesian Estimation and Posterior Distribution Calculation

    • Standard Kalman Filters

    • Smoothing Kalman Filters

    • Adaptive Kalman Filters

    • Unscented Kalman Filters

    • Particle Filters

  • Global Heuristic Optimization

    • Genetic Algorithm

  • Sensitivity Analysis

    • Using shapely sensitivities

  • Uncertainty Propagation

    • polynomial chaos expansion

    • brute monte carlo

  • Time Series Analysis

    • AutoArch

    • Auto-regressive neural networks

      • feedforward architecture generation

      • both mean and quantiles

  • Quantile K-Nearest Neighbor

  • Outlier removal

  • Linear Regressions (CPU / GPU)

  • AutoML wrappers around (AutoGluon)

  • Probablistic IoT deviation checks

    • Example: determine if incoming IoT data is no longer supported by an existing training set

  • Gaussian Process Models

    • includes templates for using physics based mean functions

    • update functionality not requiring retraining for streaming data

Requirements:

  • Supported Operating Systems: Linux, Windows

  • Python 3.10+

Installation

git clone git@ssh.gitlab.aws.dev:autonomouscomputesateam/twinstat.git
cd twinstat/dist
pip install ./*.whl

API Documentation

Auto-documentation can be found here:

https://gitlab.aws.dev/autonomouscomputesateam/twinstat/-/tree/main/docs/_build/html

Users can:

git clone git@ssh.gitlab.aws.dev:autonomouscomputesateam/twinstat.git
cd twinstat/docs/_build/html

View the index.html to review API documentation.

Example

Full tutorials to be published on AWS Samples in Q4 2023.

Short example:

from twinstat.ts_forecast.AR_NN_models import AR_quantile_neural_network
ANN = AR_quantile_neural_network(AR=ar,
                        n_exog_variables= n_exo,
                        hidden_units=16,
                        tau=safety_bias,
                        include_endog=False)
ANN.train(var, X=X, patience=50)
print("Training complete")
ANN.save_model(weights_path + '/corrector_weights')
print("model and figures saved")
ANN.plot(save_fig=True)

License

This repository is released under the MIT-0 License. See the LICENSE file for details.