Tsphinx.addnodesdocument)}( rawsourcechildren]docutils.nodessection)}(hhh](h title)}(htimeseries\_forecast packageh]h Texttimeseries_forecast package}(parenth _documenthsourceNlineNuba attributes}(ids]classes]names]dupnames]backrefs]utagnamehhh hhhjC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\docs\timeseries_forecast\timeseries_forecast.rsthKubh )}(hhh]h)}(h Submodulesh]h Submodules}(hh0hhhNhNubah}(h!]h#]h%]h']h)]uh+hhh-hhhh,hKubah}(h!] submodulesah#]h%] submodulesah']h)]uh+h hh hhhh,hKubh )}(hhh](h)}(h*timeseries\_forecast.AR\_NN\_models moduleh]h*timeseries_forecast.AR_NN_models module}(hhIhhhNhNubah}(h!]h#]h%]h']h)]uh+hhhFhhhh,hKubhindex)}(hhh]h}(h!]h#]h%]h']h)]entries](pair(module; timeseries_forecast.AR_NN_models'module-timeseries_forecast.AR_NN_modelshNtauh+hWhhFhhhNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](singleFAR_quantile_neural_network (class in timeseries_forecast.AR_NN_models);timeseries_forecast.AR_NN_models.AR_quantile_neural_networkhNtauh+hWhhFhhhNhNubhdesc)}(hhh](hdesc_signature)}(hCAR_quantile_neural_network(tau=0.5, loss_type='quantile', **kwargs)h](hdesc_annotation)}(h2[<#text: 'class'>, >]h](hclass}(hhhhhNhNubhdesc_sig_space)}(h h]h }(hhhhhNhNubah}(h!]h#]wah%]h']h)]uh+hhhubeh}(h!]h#]h%]h']h)] xml:spacepreserveuh+hhh~hhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\AR_NN_models.py:docstring of timeseries_forecast.AR_NN_models.AR_quantile_neural_networkhKubh desc_addname)}(h!timeseries_forecast.AR_NN_models.h]h!timeseries_forecast.AR_NN_models.}(hhhhhNhNubah}(h!]h#]( sig-prename descclassnameeh%]h']h)]hhuh+hhh~hhhhhKubh desc_name)}(hAR_quantile_neural_networkh]hAR_quantile_neural_network}(hhhhhNhNubah}(h!]h#](sig-namedescnameeh%]h']h)]hhuh+hhh~hhhhhKubhdesc_parameterlist)}(h'tau=0.5, loss_type='quantile', **kwargsh](hdesc_parameter)}(htau=0.5h](h desc_sig_name)}(htauh]htau}(hhhhhNhNubah}(h!]h#]nah%]h']h)]uh+hhhubhdesc_sig_operator)}(h=h]h=}(hhhhhNhNubah}(h!]h#]oah%]h']h)]uh+hhhubh inline)}(h0.5h]h0.5}(hhhhhNhNubah}(h!]h#] default_valueah%]h']h)]support_smartquotesuh+hhhubeh}(h!]h#]h%]h']h)]hhuh+hhhubh)}(hloss_type='quantile'h](h)}(h loss_typeh]h loss_type}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubh)}(h=h]h=}(hj"hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubh)}(h 'quantile'h]h 'quantile'}(hj0hhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhhubh)}(h**kwargsh](h)}(h**h]h**}(hjIhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjEubh)}(hkwargsh]hkwargs}(hjWhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjEubeh}(h!]h#]h%]h']h)]hhuh+hhhubeh}(h!]h#]h%]h']h)]hhuh+hhh~hhhhhKubeh}(h!]huah#](sig sig-objecteh%]h']h)]module timeseries_forecast.AR_NN_modelsclasshfullnameh _toc_partsjzh _toc_namehuh+h|hhhKhhyhhubh desc_content)}(hhh](h paragraph)}(hIBases: :py:class:`~twinstat.core.neural_network_base.base_neural_network`h](hBases: }(hjhhhNhNubh pending_xref)}(hB:py:class:`~twinstat.core.neural_network_base.base_neural_network`h]h literal)}(hjh]hbase_neural_network}(hjhhhNhNubah}(h!]h#](xrefpypy-classeh%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]refdoc'timeseries_forecast/timeseries_forecast refdomainjreftypeclass refexplicitrefwarn py:modulejzpy:classh reftarget5twinstat.core.neural_network_base.base_neural_networkuh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\AR_NN_models.py:docstring of timeseries_forecast.AR_NN_models.AR_quantile_neural_networkhKhjubeh}(h!]h#]h%]h']h)]uh+jhjhKhjhhubj)}(hUse a tensorflow to train an autoregressive quantile neural network. The loss seeks to minimize the weighted residual based on the quantile which causes predicitions to estimate the expectation of the quantile.h]hUse a tensorflow to train an autoregressive quantile neural network. The loss seeks to minimize the weighted residual based on the quantile which causes predicitions to estimate the expectation of the quantile.}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\AR_NN_models.py:docstring of timeseries_forecast.AR_NN_models.AR_quantile_neural_networkhKhjhhubh field_list)}(hhh](h field)}(hhh](h field_name)}(h Parametersh]h Parameters}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhhhKubh field_body)}(hhh]h bullet_list)}(hhh](h list_item)}(hhh]j)}(htau (float, optional) -- Determines the quantile [0.0-1.0] that the network will seek to follow. The default is 0.5.Example: 0.5 will result in following the median of the data where as 0.95 will attempt to cover 95% of the data.h](hliteral_strong)}(htauh]htau}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]hliteral_emphasis)}(hfloath]hfloat}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainpy refexplicitreftypej{ reftargetj refspecificjjzjhuh+jhjubj)}(h, h]h, }(hj.hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hj?hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj<ubah}(h!]h#]h%]h']h)] refdomainj) refexplicitreftypej{ reftargetjAj-jjzjhuh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubj)}(h\Determines the quantile [0.0-1.0] that the network will seek to follow. The default is 0.5.h]h\Determines the quantile [0.0-1.0] that the network will seek to follow. The default is 0.5.}(hj_hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhKhjhhubj)}(hqExample: 0.5 will result in following the median of the data where as 0.95 will attempt to cover 95% of the data.h]hqExample: 0.5 will result in following the median of the data where as 0.95 will attempt to cover 95% of the data.}(hjmhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhK hjhhubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hloss_type (str, optional) -- Determines the loss function to use while training the neural network. The default is 'quantile'. Accepts strings for any of the standard tensorflow loss functions.h](j)}(h loss_typeh]h loss_type}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hstrh]hstr}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj) refexplicitreftypej{ reftargetjj-jjzjhuh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj) refexplicitreftypej{ reftargetjj-jjzjhuh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhDetermines the loss function to use while training the neural network. The default is ‘quantile’. Accepts strings for any of the standard tensorflow loss functions.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hJtau -- The percentile to use in a quantile regression. The default is 0.5.h](j)}(htauh]htau}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh – }(hjhhhNhNubhCThe percentile to use in a quantile regression. The default is 0.5.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hJloss_type -- Any tensorflow loss or 'quantile'. The default is 'quantile'.h](j)}(h loss_typeh]h loss_type}(hj,hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj(ubh – }(hj(hhhNhNubhEAny tensorflow loss or ‘quantile’. The default is ‘quantile’.}(hj(hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj%ubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh](j)}(h Return typeh]h Return type}(hjchhhNhNubah}(h!]h#]h%]h']h)]uh+jhj`hhhKubj)}(hhh]j)}(hNone.h]j)}(hhh]hNone.}(hjxhhhNhNubah}(h!]h#]h%]h']h)] refdomainj) refexplicitreftypej{ reftargetNone.j-jjzjhuh+jhjtubah}(h!]h#]h%]h']h)]uh+jhjqubah}(h!]h#]h%]h']h)]uh+jhj`ubeh}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjhhhNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hsQload_model() (timeseries_forecast.AR_NN_models.AR_quantile_neural_network method)Ftimeseries_forecast.AR_NN_models.AR_quantile_neural_network.load_modelhNtauh+hWhjhhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\AR_NN_models.py:docstring of timeseries_forecast.AR_NN_models.AR_quantile_neural_network.load_modelhNubhx)}(hhh](h})}(h/AR_quantile_neural_network.load_model(filename)h](h)}(h load_modelh]h load_model}(hjhhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhjhhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\AR_NN_models.py:docstring of timeseries_forecast.AR_NN_models.AR_quantile_neural_network.load_modelhKubh)}(hfilenameh]h)}(hfilenameh]h)}(hfilenameh]hfilename}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubah}(h!]h#]h%]h']h)]hhuh+hhjubah}(h!]h#]h%]h']h)]hhuh+hhjhhhjhKubeh}(h!]jah#](jtjueh%]h']h)]jy timeseries_forecast.AR_NN_modelsj{hj|%AR_quantile_neural_network.load_modelj}jAR_quantile_neural_network load_modelj'AR_quantile_neural_network.load_model()uh+h|hjhKhjhhubj)}(hhh]j)}(h(load the model weights and config valuesh]h(load the model weights and config values}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhKhjhhubah}(h!]h#]h%]h']h)]uh+jhjhhhjhKubeh}(h!]h#](pymethodeh%]h']h)]domainjobjtypejdesctypejnoindex noindexentrynocontentsentryuh+hwhhhjhjhNubeh}(h!]h#]h%]h']h)]uh+jhhyhhhhhKubeh}(h!]h#](j)classeh%]h']h)]jj)jj#jj#jjjuh+hwhhhhFhNhNubeh}(h!](hf'timeseries-forecast-ar-nn-models-moduleeh#]h%]'timeseries_forecast.ar_nn_models moduleah']h)]uh+h hh hhhh,hKubh )}(hhh](h)}(h%timeseries\_forecast.GP\_model moduleh]h%timeseries_forecast.GP_model module}(hj2hhhNhNubah}(h!]h#]h%]h']h)]uh+hhj/hhhh,hKubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hd$module; timeseries_forecast.GP_model#module-timeseries_forecast.GP_modelhNtauh+hWhj/hhhNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hsAPhysicsKernel_CrackGrowth (class in timeseries_forecast.GP_model)6timeseries_forecast.GP_model.PhysicsKernel_CrackGrowthhNtauh+hWhj/hhhNhNubhx)}(hhh](h})}(hPhysicsKernel_CrackGrowth(initial_crack_length, periodic_load=0.05, C_constraint=None, m_constraint=None, batch_shape=torch.Size([]), **kwargs)h](h)}(h2[<#text: 'class'>, >]h](hclass}(hjchhhNhNubh)}(h h]h }(hjkhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjcubeh}(h!]h#]h%]h']h)]hhuh+hhj_hhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.PhysicsKernel_CrackGrowthhKubh)}(htimeseries_forecast.GP_model.h]htimeseries_forecast.GP_model.}(hjhhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhj_hhhjhKubh)}(hPhysicsKernel_CrackGrowthh]hPhysicsKernel_CrackGrowth}(hjhhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhj_hhhjhKubh)}(htinitial_crack_length, periodic_load=0.05, C_constraint=None, m_constraint=None, batch_shape=torch.Size([]), **kwargsh](h)}(hinitial_crack_lengthh]h)}(hinitial_crack_lengthh]hinitial_crack_length}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubah}(h!]h#]h%]h']h)]hhuh+hhjubh)}(hperiodic_load=0.05h](h)}(h periodic_loadh]h periodic_load}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubh)}(h=h]h=}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubh)}(h0.05h]h0.05}(hjhhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhjubh)}(hC_constraint=Noneh](h)}(h C_constrainth]h C_constraint}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubh)}(h=h]h=}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubh)}(hNoneh]hNone}(hj hhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhjubh)}(hm_constraint=Noneh](h)}(h m_constrainth]h m_constraint}(hj&hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj"ubh)}(h=h]h=}(hj4hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj"ubh)}(hNoneh]hNone}(hjBhhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhj"ubeh}(h!]h#]h%]h']h)]hhuh+hhjubh)}(hbatch_shape=torch.Size([])h](h)}(h batch_shapeh]h batch_shape}(hj[hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjWubh)}(h=h]h=}(hjihhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjWubh)}(htorch.Size([])h]htorch.Size([])}(hjwhhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhjWubeh}(h!]h#]h%]h']h)]hhuh+hhjubh)}(h**kwargsh](h)}(h**h]h**}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubh)}(hkwargsh]hkwargs}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhj_hhhjhKubeh}(h!]jZah#](jtjueh%]h']h)]jytimeseries_forecast.GP_modelj{hj|jj}jjjjuh+h|hjhKhj\hhubj)}(hhh](j)}(h,Bases: :py:class:`~gpytorch.means.mean.Mean`h](hBases: }(hjhhhNhNubj)}(h%:py:class:`~gpytorch.means.mean.Mean`h]j)}(hjh]hMean}(hjhhhNhNubah}(h!]h#](jpypy-classeh%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]refdocj refdomainjreftypeclass refexplicitrefwarnjjjjjgpytorch.means.mean.Meanuh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.PhysicsKernel_CrackGrowthhKhjubeh}(h!]h#]h%]h']h)]uh+jhjhKhjhhubj)}(hThis object serves as an example of including a physics function in the Gaussian Process. Pytorch will optimize the registered parameters including the defined constraints.h]hThis object serves as an example of including a physics function in the Gaussian Process. Pytorch will optimize the registered parameters including the defined constraints.}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.PhysicsKernel_CrackGrowthhKhjhhubj)}(hhh](j)}(hhh](j)}(h Parametersh]h Parameters}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhjhKubj)}(hhh]j)}(hhh](j)}(hhh]j)}(hbinitial_crack_length (float) -- Initial crack size to be inserted as a constant in Paris equation.h](j)}(hinitial_crack_lengthh]hinitial_crack_length}(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hj7hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj4ubah}(h!]h#]h%]h']h)] refdomainpy refexplicitreftypej{ reftargetj9j-jjjjuh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhBInitial crack size to be inserted as a constant in Paris equation.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(h^periodic_load (float, optional) -- Cyclic load causing crack propagation. The default is 0.05.h](j)}(h periodic_loadh]h periodic_load}(hjohhhNhNubah}(h!]h#]h%]h']h)]uh+jhjkubh (}(hjkhhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainjL refexplicitreftypej{ reftargetjj-jjjjuh+jhjkubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjkubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainjL refexplicitreftypej{ reftargetjj-jjjjuh+jhjkubh)}(hjkhhhNhNubh – }(hjkhhhNhNubh;Cyclic load causing crack propagation. The default is 0.05.}(hjkhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjhubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(h_batch_shape (int, optional) -- Size of batches to train on in SGD. The default is torch.Size().h](j)}(h batch_shapeh]h batch_shape}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hinth]hint}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainjL refexplicitreftypej{ reftargetjj-jjjjuh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainjL refexplicitreftypej{ reftargetj$j-jjjjuh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubh@Size of batches to train on in SGD. The default is torch.Size().}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh](j)}(h Return typeh]h Return type}(hjghhhNhNubah}(h!]h#]h%]h']h)]uh+jhjdhjhKubj)}(hhh]j)}(hNone.h]j)}(hhh]hNone.}(hj|hhhNhNubah}(h!]h#]h%]h']h)] refdomainjL refexplicitreftypej{ reftargetNone.j-jjjjuh+jhjxubah}(h!]h#]h%]h']h)]uh+jhjuubah}(h!]h#]h%]h']h)]uh+jhjdubeh}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjhhhNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hsCC (timeseries_forecast.GP_model.PhysicsKernel_CrackGrowth property)8timeseries_forecast.GP_model.PhysicsKernel_CrackGrowth.ChNtauh+hWhjhhhNhNubhx)}(hhh](h})}(hPhysicsKernel_CrackGrowth.Ch](h)}(h5[<#text: 'property'>, >]h](hproperty}(hjhhhNhNubh)}(h h]h }(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhjhhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.PhysicsKernel_CrackGrowth.ChKubh)}(hCh]hC}(hjhhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhjhhhjhKubeh}(h!]jah#](jtjueh%]h']h)]jytimeseries_forecast.GP_modelj{jj|PhysicsKernel_CrackGrowth.Cj}jPhysicsKernel_CrackGrowthjjjuh+h|hjhKhjhhubj)}(hhh]h}(h!]h#]h%]h']h)]uh+jhjhhhjhKubeh}(h!]h#](pypropertyeh%]h']h)]jjjjjjjjjuh+hwhhhjhNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hsCm (timeseries_forecast.GP_model.PhysicsKernel_CrackGrowth property)8timeseries_forecast.GP_model.PhysicsKernel_CrackGrowth.mhNtauh+hWhjhhhNhNubhx)}(hhh](h})}(hPhysicsKernel_CrackGrowth.mh](h)}(h5[<#text: 'property'>, >]h](hproperty}(hjhhhNhNubh)}(h h]h }(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhjhhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.PhysicsKernel_CrackGrowth.mhKubh)}(hmh]hm}(hj3hhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhjhhhj2hKubeh}(h!]j 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jobjectuh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.GPModelhKhj ubeh}(h!]h#]h%]h']h)]uh+jhj hKhj hhubj)}(hUsing gpytorch as the backend solver, this object constructs a Gaussian Process of varying levels of complexity, which includes incorporation of physics objects to both resolve the covaraince and calibrate the model inputs.h]hUsing gpytorch as the backend solver, this object constructs a Gaussian Process of varying levels of complexity, which includes incorporation of physics objects to both resolve the covaraince and calibrate the model inputs.}(hj hhhNhNubah}(h!]h#]h%]h']h)]uh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.GPModelhKhj hhubj)}(hcThe main goal here is bring scikit ease of use to the powerful scaling and flexibility of gpytorch.h]hcThe main goal here is bring scikit ease 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The default is 0.1.h](j)}(hlrh]hlr}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubh.Learning rate used in SGD. The default is 0.1.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hOmodel_type (str, optional) -- basic or spectral_mixing. The default is 'basic'.h](j)}(h model_typeh]h model_type}(hj7hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj3ubh (}(hj3hhhNhNubj)}(hhh]j)}(hstrh]hstr}(hjLhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjIubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjNj-jj jj# uh+jhj3ubj)}(h, h]h, }(hjdhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj3ubj)}(hhh]j)}(hoptionalh]hoptional}(hjuhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjrubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjwj-jj jj# uh+jhj3ubh)}(hj3hhhNhNubh – }(hj3hhhNhNubh5basic or spectral_mixing. The default is ‘basic’.}(hj3hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj0ubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hmean_function (str, optional) -- 'constant','linear', or custom object such as the 'PhysicsKernel_CrackGrowth'. The default is 'constant'.h](j)}(h mean_functionh]h mean_function}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hstrh]hstr}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhy‘constant’,’linear’, or custom object such as the ‘PhysicsKernel_CrackGrowth’. The default is ‘constant’.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hglikelihood_function (str, optional) -- Only supports gaussian at the moment. The default is 'gaussian'.h](j)}(hlikelihood_functionh]hlikelihood_function}(hj!hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hstrh]hstr}(hj6hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj3ubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetj8j-jj jj# uh+jhjubj)}(h, h]h, }(hjNhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hj_hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj\ubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjaj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhDOnly supports gaussian at the moment. The default is ‘gaussian’.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hrlengthscale_constraint (float, optional) -- Constraint used in the lengthscale of the kernel. The default is None.h](j)}(hlengthscale_constrainth]hlengthscale_constraint}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhFConstraint used in the lengthscale of the kernel. The default is None.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hjoutputscale_constraint (float, optional) -- Constraint used in the prediction output. The default is None.h](j)}(houtputscale_constrainth]houtputscale_constraint}(hj hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hj hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetj"j-jj jj# uh+jhjubj)}(h, h]h, }(hj8hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjIhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjFubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjKj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubh>Constraint used in the prediction output. The default is None.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hperiodic_period_constraint (float, optional) -- Constraint used in the period of the periodic kernel if present. The default is None.h](j)}(hperiodic_period_constrainth]hperiodic_period_constraint}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj|ubh (}(hj|hhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhj|ubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj|ubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhj|ubh)}(hj|hhhNhNubh – }(hj|hhhNhNubhUConstraint used in the period of the periodic kernel if present. The default is None.}(hj|hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjyubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hperiodic_lengthscale_constraint (float, optional) -- Constraint used in the lengthscale of the periodic kernel if present. The default is None.h](j)}(hperiodic_lengthscale_constrainth]hperiodic_lengthscale_constraint}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hj hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetj j-jj jj# uh+jhjubj)}(h, h]h, }(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hj3hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj0ubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetj5j-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhZConstraint used in the lengthscale of the periodic kernel if present. The default is None.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hlikelihood_noise_constraint (float, optional) -- Constraint used in the magnitude of the noise adder to the covariance matrix. The default is 1e-4.h](j)}(hlikelihood_noise_constrainth]hlikelihood_noise_constraint}(hjjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjfubh (}(hjfhhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj|ubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjfubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjfubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjfubh)}(hjfhhhNhNubh – }(hjfhhhNhNubhbConstraint used in the magnitude of the noise adder to the covariance matrix. The default is 1e-4.}(hjfhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjcubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hause_cholesky (bool, optional) -- Use cholesky decompisition during training. The default is True.h](j)}(h use_choleskyh]h use_cholesky}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hboolh]hbool}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubj)}(h, h]h, }(hj hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubh@Use cholesky decompisition during training. The default is True.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hdperiodic (bool, optional) -- If true add a periodic kernel to the base_kernel. The default is False.h](j)}(hperiodich]hperiodic}(hjThhhNhNubah}(h!]h#]h%]h']h)]uh+jhjPubh (}(hjPhhhNhNubj)}(hhh]j)}(hboolh]hbool}(hjihhhNhNubah}(h!]h#]h%]h']h)]uh+jhjfubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjkj-jj jj# uh+jhjPubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjPubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjPubh)}(hjPhhhNhNubh – }(hjPhhhNhNubhGIf true add a periodic kernel to the base_kernel. The default is False.}(hjPhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjMubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(h/n_mixtures (int, optional) -- The default is 4.h](j)}(h n_mixturesh]h n_mixtures}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hinth]hint}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetj j-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhThe default is 4.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(habase_kernel (str, optional) -- Base kernel to use in the covariance matrix. The default is 'RBF'.h](j)}(h base_kernelh]h base_kernel}(hj>hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj:ubh (}(hj:hhhNhNubj)}(hhh]j)}(hstrh]hstr}(hjShhhNhNubah}(h!]h#]h%]h']h)]uh+jhjPubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjUj-jj jj# uh+jhj:ubj)}(h, h]h, }(hjkhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj:ubj)}(hhh]j)}(hoptionalh]hoptional}(hj|hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjyubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetj~j-jj jj# uh+jhj:ubh)}(hj:hhhNhNubh – }(hj:hhhNhNubhFBase kernel to use in the covariance matrix. The default is ‘RBF’.}(hj:hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj7ubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(htrain_auto_stop (bool, optional) -- If true, stop training after early_stoppage_iterations of no improvement. The default is True.h](j)}(htrain_auto_stoph]htrain_auto_stop}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hboolh]hbool}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubh^If true, stop training after early_stoppage_iterations of no improvement. The default is True.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(huauto_stop_tol (float, optional) -- If absolute change in loss is below this value, stop traning. The default is 1e-5.h](j)}(h auto_stop_tolh]h auto_stop_tol}(hj(hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj$ubh (}(hj$hhhNhNubj)}(hhh]j)}(hfloath]hfloat}(hj=hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj:ubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetj?j-jj jj# uh+jhj$ubj)}(h, h]h, }(hjUhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj$ubj)}(hhh]j)}(hoptionalh]hoptional}(hjfhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjcubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjhj-jj jj# uh+jhj$ubh)}(hj$hhhNhNubh – }(hj$hhhNhNubhRIf absolute change in loss is below this value, stop traning. The default is 1e-5.}(hj$hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj!ubah}(h!]h#]h%]h']h)]uh+jhj'ubj)}(hhh]j)}(hearly_stoppage_iterations (int, optional) -- If during training the loss does not improve within this many iterations, stop training. The default is 100.h](j)}(hearly_stoppage_iterationsh]hearly_stoppage_iterations}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hinth]hint}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetjj-jj jj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubhlIf during training the loss does not improve within this many iterations, stop training. The default is 100.}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj'ubeh}(h!]h#]h%]h']h)]uh+jhj$ubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh](j)}(h Return typeh]h Return type}(hj hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhj hKubj)}(hhh]j)}(hNone.h]j)}(hhh]hNone.}(hj5hhhNhNubah}(h!]h#]h%]h']h)] refdomainj[ refexplicitreftypej{ reftargetNone.j-jj jj# uh+jhj1ubah}(h!]h#]h%]h']h)]uh+jhj.ubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhj hhhNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hs5train() (timeseries_forecast.GP_model.GPModel method)*timeseries_forecast.GP_model.GPModel.trainhNtauh+hWhj hhhNhNubhx)}(hhh](h})}(hGPModel.train(training_iter)h](h)}(htrainh]htrain}(hjthhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhjphhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.GPModel.trainhKubh)}(h training_iterh]h)}(h training_iterh]h)}(h training_iterh]h training_iter}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubah}(h!]h#]h%]h']h)]hhuh+hhjubah}(h!]h#]h%]h']h)]hhuh+hhjphhhjhKubeh}(h!]jkah#](jtjueh%]h']h)]jytimeseries_forecast.GP_modelj{j# j| GPModel.trainj}jGPModeltrainjGPModel.train()uh+h|hjhKhjmhhubj)}(hhh]j)}(hhh](j)}(hhh](j)}(h Return typeh]h Return type}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhjhKubj)}(hhh]j)}(hNoneh]j)}(h:py:obj:`None`h]j)}(hjh]hNone}(hjhhhNhNubah}(h!]h#](jpypy-objeh%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]refdocj refdomainjreftypeobj refexplicitrefwarnjjjj# jNoneuh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.GPModel.trainhKhjhhubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh](j)}(h Parametersh]h Parameters}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhjhKubj)}(hhh]j)}(h?training_iter (int) -- Number of training iterations to performh](j)}(h training_iterh]h training_iter}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hinth]hint}(hj/hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj,ubah}(h!]h#]h%]h']h)] refdomainpy refexplicitreftypej{ reftargetj1j-jjjj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubh(Number of training iterations to perform}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh](j)}(h Return typeh]h Return type}(hjihhhNhNubah}(h!]h#]h%]h']h)]uh+jhjfhjhKubj)}(hhh]j)}(hNoneh]j)}(hhh]hNone}(hj~hhhNhNubah}(h!]h#]h%]h']h)] refdomainjD refexplicitreftypej{ reftargetNonej-jjjj# uh+jhjzubah}(h!]h#]h%]h']h)]uh+jhjwubah}(h!]h#]h%]h']h)]uh+jhjfubeh}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjmhhhjhKubeh}(h!]h#](jDmethodeh%]h']h)]jjDjjjjjjjuh+hwhhhj hNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hsDupdate_training_data() (timeseries_forecast.GP_model.GPModel method)9timeseries_forecast.GP_model.GPModel.update_training_datahNtauh+hWhj hhhNhNubhx)}(hhh](h})}(h7GPModel.update_training_data(newX, newy, replace=False)h](h)}(hupdate_training_datah]hupdate_training_data}(hjhhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhjhhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.GPModel.update_training_datahKubh)}(hnewX, newy, replace=Falseh](h)}(hnewXh]h)}(hnewXh]hnewX}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubah}(h!]h#]h%]h']h)]hhuh+hhjubh)}(hnewyh]h)}(hnewyh]hnewy}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjubah}(h!]h#]h%]h']h)]hhuh+hhjubh)}(h replace=Falseh](h)}(hreplaceh]hreplace}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj ubh)}(h=h]h=}(hjhhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj ubh)}(hFalseh]hFalse}(hj-hhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhj ubeh}(h!]h#]h%]h']h)]hhuh+hhjubeh}(h!]h#]h%]h']h)]hhuh+hhjhhhjhKubeh}(h!]jah#](jtjueh%]h']h)]jytimeseries_forecast.GP_modelj{j# j|GPModel.update_training_dataj}jNGPModelupdate_training_datajGPModel.update_training_data()uh+h|hjhKhjhhubj)}(hhh](j)}(hUpdate the training data stored within the object. When training, the GP will use this data to refine the fitting parameters. Can be used to refine existing models instead of retraining from scratch.h]hUpdate the training data stored within the object. When training, the GP will use this data to refine the fitting parameters. Can be used to refine existing models instead of retraining from scratch.}(hjWhhhNhNubah}(h!]h#]h%]h']h)]uh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.GPModel.update_training_datahKhjThhubj)}(hhh](j)}(hhh](j)}(h Return typeh]h Return type}(hjlhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjihjhKubj)}(hhh]j)}(hNoneh]j)}(h:py:obj:`None`h]j)}(hjh]hNone}(hjhhhNhNubah}(h!]h#](jpypy-objeh%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]refdocj refdomainjreftypeobj refexplicitrefwarnjjNjj# jNoneuh+jhjehKhj}hhubah}(h!]h#]h%]h']h)]uh+jhjzubah}(h!]h#]h%]h']h)]uh+jhjiubeh}(h!]h#]h%]h']h)]uh+jhjfubj)}(hhh](j)}(h Parametersh]h Parameters}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhjhKubj)}(hhh]j)}(hhh](j)}(hhh]j)}(hnewX (np.array) -- h](j)}(hnewXh]hnewX}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hnp.arrayh]hnp.array}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainpy refexplicitreftypej{ reftargetjj-jjNjj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hnewy (np.array) -- h](j)}(hnewyh]hnewy}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubh (}(hjhhhNhNubj)}(hhh]j)}(hnp.arrayh]hnp.array}(hj/hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj,ubah}(h!]h#]h%]h']h)] refdomainj refexplicitreftypej{ reftargetj1j-jjNjj# uh+jhjubh)}(hjhhhNhNubh – }(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(hareplace (bool, optional) -- Append to the existing training set or replace. The default is False.h](j)}(hreplaceh]hreplace}(hjbhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj^ubh (}(hj^hhhNhNubj)}(hhh]j)}(hboolh]hbool}(hjwhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjtubah}(h!]h#]h%]h']h)] refdomainj refexplicitreftypej{ reftargetjyj-jjNjj# uh+jhj^ubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhj^ubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainj refexplicitreftypej{ reftargetjj-jjNjj# uh+jhj^ubh)}(hj^hhhNhNubh – }(hj^hhhNhNubhEAppend to the existing training set or replace. The default is False.}(hj^hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj[ubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjfubj)}(hhh](j)}(h Return typeh]h Return type}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhjhKubj)}(hhh]j)}(hNoneh]j)}(hhh]hNone}(hjhhhNhNubah}(h!]h#]h%]h']h)] refdomainj refexplicitreftypej{ reftargetNonej-jjNjj# uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjfubeh}(h!]h#]h%]h']h)]uh+jhjThhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjhhhjhKubeh}(h!]h#](jmethodeh%]h']h)]jjjj-jj-jjjuh+hwhhhj hNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hsj-jjjj# uh+jhj#ubh)}(hj#hhhNhNubh – }(hj#hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj ubah}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh]j)}(huncertainty (str, optional) -- Either sets the uncertainty to be based on the confidence (95%) of the mean prediction or the prediction interval (95%), which bounds were we would expect to see a newly measured value. The default is 'confidence'.h](j)}(h uncertaintyh]h uncertainty}(hjphhhNhNubah}(h!]h#]h%]h']h)]uh+jhjlubh (}(hjlhhhNhNubj)}(hhh]j)}(hstrh]hstr}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainjQ refexplicitreftypej{ reftargetjj-jjjj# uh+jhjlubj)}(h, h]h, }(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjlubj)}(hhh]j)}(hoptionalh]hoptional}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)] refdomainjQ refexplicitreftypej{ reftargetjj-jjjj# uh+jhjlubh)}(hjlhhhNhNubh – }(hjlhhhNhNubhEither sets the uncertainty to be based on the confidence (95%) of the mean prediction or the prediction interval (95%), which bounds were we would expect to see a newly measured value. The default is ‘confidence’.}(hjlhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjiubah}(h!]h#]h%]h']h)]uh+jhjubeh}(h!]h#]h%]h']h)]uh+jhjubah}(h!]h#]h%]h']h)]uh+jhj ubeh}(h!]h#]h%]h']h)]uh+jhjubj)}(hhh](j)}(hReturnsh]hReturns}(hjhhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhjThKubj)}(hhh]j)}(hXdobserved_pred --gpytorch object that includes the attributes/methods:confidence_region mean varianceC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\GP_model.py:docstring of timeseries_forecast.GP_model.GPModel.get_estimate:18: (WARNING/2) Block quote ends without a blank line; unexpected unindent.Example: observed_pred = GP.get_estimate(X, uncertainty='confidence') # Get upper and lower confidence bounds lower, upper = observed_pred.confidence_region() # Get the expectation mean = observed_pred.mean.numpy() # Get the variance mean = observed_pred.variance.numpy()h](j)}(h**observed_pred** --h](jd )}(h**observed_pred**h]h observed_pred}(hj hhhNhNubah}(h!]h#]h%]h']h)]uh+jc hjubh –}(hjhhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjhK hjhhubj)}(h5gpytorch object that includes the attributes/methods:h]h5gpytorch object that includes the attributes/methods:}(hj$hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhK hjhhubh block_quote)}(hconfidence_region mean varianceh]j)}(hconfidence_region mean varianceh]hconfidence_region mean variance}(hj8hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjhKhj4ubah}(h!]h#]h%]h']h)]uh+j2hjhKhjhhubh definition_list)}(hhh]h definition_list_item)}(hXExample: observed_pred = GP.get_estimate(X, uncertainty='confidence') # Get upper and lower confidence bounds lower, upper = observed_pred.confidence_region() # Get the expectation mean = observed_pred.mean.numpy() # Get the variance mean = observed_pred.variance.numpy()h](h term)}(hExample:h]hExample:}(hjYhhhNhNubah}(h!]h#]h%]h']h)]uh+jWhjhKhjSubh definition)}(hhh](j)}(h, >]h](hclass}(hj hhhNhNubh)}(h h]h }(hj hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj ubeh}(h!]h#]h%]h']h)]hhuh+hhj hhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\parametric.py:docstring of timeseries_forecast.parametric.ts_modelhKubh)}(htimeseries_forecast.parametric.h]htimeseries_forecast.parametric.}(hj hhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhj hhhj hKubh)}(hts_modelh]hts_model}(hj hhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhj hhhj hKubh)}(h volatility_model=False, **kwargsh](h)}(hvolatility_model=Falseh](h)}(hvolatility_modelh]hvolatility_model}(hj!hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj!ubh)}(h=h]h=}(hj!hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj!ubh)}(hFalseh]hFalse}(hj!!hhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhj!ubeh}(h!]h#]h%]h']h)]hhuh+hhj ubh)}(h**kwargsh](h)}(h**h]h**}(hj:!hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj6!ubh)}(hkwargsh]hkwargs}(hjH!hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj6!ubeh}(h!]h#]h%]h']h)]hhuh+hhj ubeh}(h!]h#]h%]h']h)]hhuh+hhj hhhj hKubeh}(h!]j ah#](jtjueh%]h']h)]jytimeseries_forecast.parametricj{hj|j j}jh!j jj uh+h|hj hKhj hhubj)}(hhh](j)}(hBases: :py:class:`object`h](hBases: }(hjm!hhhNhNubj)}(h:py:class:`object`h]j)}(hjw!h]hobject}(hjy!hhhNhNubah}(h!]h#](jpypy-classeh%]h']h)]uh+jhju!ubah}(h!]h#]h%]h']h)]refdocj refdomainj!reftypeclass refexplicitrefwarnjjh!jj jobjectuh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\parametric.py:docstring of timeseries_forecast.parametric.ts_modelhKhjm!ubeh}(h!]h#]h%]h']h)]uh+jhj!hKhjj!hhubj)}(h?Provides a pass through interface for AutoARIMA or ARCH models.h]h?Provides a pass through interface for AutoARIMA or ARCH models.}(hj!hhhNhNubah}(h!]h#]h%]h']h)]uh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\parametric.py:docstring of timeseries_forecast.parametric.ts_modelhKhjj!hhubj)}(hUsers should see the following links for full API inputs to these functions. This object currently provides no additional value for ARIMA.h]hUsers should see the following links for full API inputs to these functions. This object currently provides no additional value for ARIMA.}(hj!hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj!hKhjj!hhubj)}(h@Main functionality is the additional of AutoARCH grid searching.h]h@Main functionality is the additional of AutoARCH grid searching.}(hj!hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj!hKhjj!hhubjM)}(hhh](jR)}(hAutoARIMA documentation: http://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html#pmdarima.arima.AutoARIMA h](jX)}(hAutoARIMA documentation:h]hAutoARIMA documentation:}(hj!hhhNhNubah}(h!]h#]h%]h']h)]uh+jWhj!hK hj!ubjh)}(hhh]j)}(hhhttp://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html#pmdarima.arima.AutoARIMAh]h reference)}(hj!h]hhhttp://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html#pmdarima.arima.AutoARIMA}(hj!hhhNhNubah}(h!]h#]h%]h']h)]refurij!uh+j!hj!ubah}(h!]h#]h%]h']h)]uh+jhj!hK hj!ubah}(h!]h#]h%]h']h)]uh+jghj!ubeh}(h!]h#]h%]h']h)]uh+jQhj!hK hj!ubjR)}(h;Arch documentation: https://arch.readthedocs.io/en/latest/ h](jX)}(hArch documentation:h]hArch documentation:}(hj "hhhNhNubah}(h!]h#]h%]h']h)]uh+jWhj!hKhj"ubjh)}(hhh]j)}(h&https://arch.readthedocs.io/en/latest/h]j!)}(hj"h]h&https://arch.readthedocs.io/en/latest/}(hj"hhhNhNubah}(h!]h#]h%]h']h)]refurij"uh+j!hj"ubah}(h!]h#]h%]h']h)]uh+jhj!hKhj"ubah}(h!]h#]h%]h']h)]uh+jghj"ubeh}(h!]h#]h%]h']h)]uh+jQhj!hKhj!hhubeh}(h!]h#]h%]h']h)]uh+jLhjj!hhhj!hK ubj)}(hhh]j)}(hhh](j)}(h Parametersh]h Parameters}(hjJ"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjG"hj hKubj)}(hhh]j)}(hvolatility_model (bool, optional) -- Determines if an ARIMA or ARCH model should be used. Default is False, which uses an ARIMA model.h](j)}(hvolatility_modelh]hvolatility_model}(hj_"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj["ubh (}(hj["hhhNhNubj)}(hhh]j)}(hboolh]hbool}(hjt"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjq"ubah}(h!]h#]h%]h']h)] refdomainpy refexplicitreftypej{ reftargetjv"j-jjh!jj uh+jhj["ubj)}(h, h]h, }(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj["ubj)}(hhh]j)}(hoptionalh]hoptional}(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj"ubah}(h!]h#]h%]h']h)] refdomainj" refexplicitreftypej{ reftargetj"j-jjh!jj uh+jhj["ubh)}(hj["hhhNhNubh – }(hj["hhhNhNubhaDetermines if an ARIMA or ARCH model should be used. Default is False, which uses an ARIMA model.}(hj["hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjX"ubah}(h!]h#]h%]h']h)]uh+jhjG"ubeh}(h!]h#]h%]h']h)]uh+jhjD"ubah}(h!]h#]h%]h']h)]uh+jhjj!hhhNhNubh rubric)}(hExamplesh]hExamples}(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+j"hjj!hhhj!hKubj)}(hIn this example, an ARIMA model is automatically selected that best fits the provided 'y' data. The automation performs a parallized grid search with the max AR lags of 8, max MA lags of 3, and 1 level of signal differencing is used.h]hIn this example, an ARIMA model is automatically selected that best fits the provided ‘y’ data. The automation performs a parallized grid search with the max AR lags of 8, max MA lags of 3, and 1 level of signal differencing is used.}(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj!hKhjj!hhubj)}(hiThe predict function will start from the end of the training data and forecast n_periods into the future.h]hiThe predict function will start from the end of the training data and forecast n_periods into the future.}(hj"hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj!hKhjj!hhubh literal_block)}(hfrom twinstat.timeseries_forecast.parametric import ts_model TS = ts_model(max_p =8, max_q=3, D=1, n_jobs=-1, stepwise=False) TS.model.fit(y) TS.model.summary() TS.model.predict(n_periods=5)h]hfrom twinstat.timeseries_forecast.parametric import ts_model TS = ts_model(max_p =8, max_q=3, D=1, n_jobs=-1, stepwise=False) TS.model.fit(y) TS.model.summary() TS.model.predict(n_periods=5)}hj#sbah}(h!]h#]h%]h']h)]hhforcelanguagepythonhighlight_args}uh+j#hj!hKhjj!hhubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hs7plot() (timeseries_forecast.parametric.ts_model method),timeseries_forecast.parametric.ts_model.plothNtauh+hWhjj!hhhNhNubhx)}(hhh](h})}(hts_model.plot(series, lags=40)h](h)}(hploth]hplot}(hj0#hhhNhNubah}(h!]h#](hheh%]h']h)]hhuh+hhj,#hhhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\parametric.py:docstring of timeseries_forecast.parametric.ts_model.plothKubh)}(hseries, lags=40h](h)}(hseriesh]h)}(hseriesh]hseries}(hjG#hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhjC#ubah}(h!]h#]h%]h']h)]hhuh+hhj?#ubh)}(hlags=40h](h)}(hlagsh]hlags}(hj_#hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj[#ubh)}(h=h]h=}(hjm#hhhNhNubah}(h!]h#]hah%]h']h)]uh+hhj[#ubh)}(h40h]h40}(hj{#hhhNhNubah}(h!]h#]jah%]h']h)]support_smartquotesuh+hhj[#ubeh}(h!]h#]h%]h']h)]hhuh+hhj?#ubeh}(h!]h#]h%]h']h)]hhuh+hhj,#hhhj>#hKubeh}(h!]j'#ah#](jtjueh%]h']h)]jytimeseries_forecast.parametricj{j j| ts_model.plotj}j#ts_modelplotjts_model.plot()uh+h|hj>#hKhj)#hhubj)}(hhh](j3)}(hXSome useful diagnostic plots to determine what the proper AR and MA terms would be for the provided data. Note that the a decaying ACF will determine MA and a decarying PACF will determine AR. Users may want to increase the number of lags shown, to assess if a season lag needs to be included in the models. Users are recommended to see a time series text to fully understand how to use these plots. h](j)}(hiSome useful diagnostic plots to determine what the proper AR and MA terms would be for the provided data.h]hiSome useful diagnostic plots to determine what the proper AR and MA terms would be for the provided data.}(hj#hhhNhNubah}(h!]h#]h%]h']h)]uh+jhC:\Users\rpivovar\Desktop\workingfolder\projects\brainbox\twinstat\timeseries_forecast\parametric.py:docstring of timeseries_forecast.parametric.ts_model.plothKhj#ubj)}(hNote that the a decaying ACF will determine MA and a decarying PACF will determine AR. Users may want to increase the number of lags shown, to assess if a season lag needs to be included in the models.h]hNote that the a decaying ACF will determine MA and a decarying PACF will determine AR. Users may want to increase the number of lags shown, to assess if a season lag needs to be included in the models.}(hj#hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj#hKhj#ubj)}(h[Users are recommended to see a time series text to fully understand how to use these plots.h]h[Users are recommended to see a time series text to fully understand how to use these plots.}(hj#hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj#hK hj#ubeh}(h!]h#]h%]h']h)]uh+j2hj#hKhj#hhubj)}(hhh](j)}(hhh](j)}(h Parametersh]h Parameters}(hj#hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj#hj>#hKubj)}(hhh]j)}(hhh](j)}(hhh]j)}(h"series (np.array) -- Data to plot.h](j)}(hseriesh]hseries}(hj#hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj#ubh (}(hj#hhhNhNubj)}(hhh]j)}(hnp.arrayh]hnp.array}(hj$hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj $ubah}(h!]h#]h%]h']h)] refdomainpy refexplicitreftypej{ reftargetj$j-jj#jj uh+jhj#ubh)}(hj#hhhNhNubh – }(hj#hhhNhNubh Data to plot.}(hj#hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj#ubah}(h!]h#]h%]h']h)]uh+jhj#ubj)}(hhh]j)}(hQlags (int, optional) -- Number of lags to include in the plot. The default is 40.h](j)}(hlagsh]hlags}(hjH$hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjD$ubh (}(hjD$hhhNhNubj)}(hhh]j)}(hinth]hint}(hj]$hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjZ$ubah}(h!]h#]h%]h']h)] refdomainj%$ refexplicitreftypej{ reftargetj_$j-jj#jj uh+jhjD$ubj)}(h, h]h, }(hju$hhhNhNubah}(h!]h#]h%]h']h)]uh+jhjD$ubj)}(hhh]j)}(hoptionalh]hoptional}(hj$hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj$ubah}(h!]h#]h%]h']h)] refdomainj%$ refexplicitreftypej{ reftargetj$j-jj#jj uh+jhjD$ubh)}(hjD$hhhNhNubh – }(hjD$hhhNhNubh9Number of lags to include in the plot. The default is 40.}(hjD$hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhjA$ubah}(h!]h#]h%]h']h)]uh+jhj#ubeh}(h!]h#]h%]h']h)]uh+jhj#ubah}(h!]h#]h%]h']h)]uh+jhj#ubeh}(h!]h#]h%]h']h)]uh+jhj#ubj)}(hhh](j)}(hReturnsh]hReturns}(hj$hhhNhNubah}(h!]h#]h%]h']h)]uh+jhj$hj>#hKubj)}(hhh]j)}(hNone. rtype: Noneh]j)}(hhh](j)}(h*None.*h]j)}(hj$h]h emphasis)}(hj$h]hNone.}(hj$hhhNhNubah}(h!]h#]h%]h']h)]uh+j$hj$ubah}(h!]h#]h%]h']h)]uh+jhj#hKhj$ubah}(h!]h#]h%]h']h)]uh+jhj$ubj)}(hrtype: :py:obj:`None`h]j)}(hj%h](hrtype: }(hj %hhhNhNubj)}(h:py:obj:`None`h]j)}(hj%h]hNone}(hj%hhhNhNubah}(h!]h#](jpypy-objeh%]h']h)]uh+jhj%ubah}(h!]h#]h%]h']h)]refdocj refdomainj%reftypeobj refexplicitrefwarnjj#jj jNoneuh+jhj#hKhj %ubeh}(h!]h#]h%]h']h)]uh+jhj#hKhj%ubah}(h!]h#]h%]h']h)]uh+jhj$ubeh}(h!]h#]h%]h']h)]bullet*uh+jhj#hKhj$hhubah}(h!]h#]h%]h']h)]uh+jhj$ubah}(h!]h#]h%]h']h)]uh+jhj$ubeh}(h!]h#]h%]h']h)]uh+jhj#ubeh}(h!]h#]h%]h']h)]uh+jhj#hhhNhNubeh}(h!]h#]h%]h']h)]uh+jhj)#hhhj>#hKubeh}(h!]h#](j%$methodeh%]h']h)]jj%$jje%jje%jjjuh+hwhhhjj!hNhNubhX)}(hhh]h}(h!]h#]h%]h']h)]entries](hs