diff --git a/docs/readthedocs/source/doc/PythonAPI/Chronos/automodels.rst b/docs/readthedocs/source/doc/PythonAPI/Chronos/automodels.rst index faff1a74..e7656c6a 100644 --- a/docs/readthedocs/source/doc/PythonAPI/Chronos/automodels.rst +++ b/docs/readthedocs/source/doc/PythonAPI/Chronos/automodels.rst @@ -5,35 +5,52 @@ AutoTCN ------------------------------------------- AutoTCN is a TCN forecasting model with Auto tuning. -Other API follows its base class(BaseAutomodel). -.. automodule:: bigdl.chronos.autots.model.auto_tcn - :members: - :undoc-members: - :show-inheritance: - + +.. tabs:: + + .. tab:: PyTorch + + .. automodule:: bigdl.chronos.autots.model.auto_tcn + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + AutoLSTM ---------------------------------------- AutoLSTM is an LSTM forecasting model with Auto tuning. -Other API follows its base class(BaseAutomodel). -.. automodule:: bigdl.chronos.autots.model.auto_lstm - :members: - :undoc-members: - :show-inheritance: + +.. tabs:: + + .. tab:: PyTorch + + .. automodule:: bigdl.chronos.autots.model.auto_lstm + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + AutoSeq2Seq ---------------------------------------- AutoSeq2Seq is an Seq2Seq forecasting model with Auto tuning. -Other API follows its base class(BaseAutomodel). -.. automodule:: bigdl.chronos.autots.model.auto_seq2seq - :members: - :undoc-members: - :show-inheritance: + +.. tabs:: + + .. tab:: PyTorch + + .. automodule:: bigdl.chronos.autots.model.auto_seq2seq + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + AutoARIMA ---------------------------------------- @@ -44,6 +61,8 @@ AutoARIMA is an ARIMA forecasting model with Auto tuning. :members: :undoc-members: :show-inheritance: + :inherited-members: + AutoProphet ---------------------------------------- @@ -54,11 +73,4 @@ AutoProphet is a Prophet forecasting model with Auto tuning. :members: :undoc-members: :show-inheritance: - -BaseAutomodel ------------------------------------------------------------- -AutoLSTM, AutoSeq2Seq and AutoTCN all follow the same API as stated below. - -.. autoclass:: bigdl.chronos.autots.model.base_automodel.BaseAutomodel - :members: - :show-inheritance: + :inherited-members: diff --git a/docs/readthedocs/source/doc/PythonAPI/Chronos/autotsestimator.rst b/docs/readthedocs/source/doc/PythonAPI/Chronos/autotsestimator.rst index 561026a6..9bacc519 100644 --- a/docs/readthedocs/source/doc/PythonAPI/Chronos/autotsestimator.rst +++ b/docs/readthedocs/source/doc/PythonAPI/Chronos/autotsestimator.rst @@ -7,10 +7,14 @@ AutoTSEstimator Automated TimeSeries Estimator for time series forecasting task. AutoTSEstimator will replace AutoTSTrainer in later version. -.. automodule:: bigdl.chronos.autots.autotsestimator - :members: - :undoc-members: - :show-inheritance: +.. tabs:: + + .. tab:: PyTorch + + .. automodule:: bigdl.chronos.autots.autotsestimator + :members: + :undoc-members: + :show-inheritance: TSPipeline @@ -19,7 +23,12 @@ TSPipeline TSPipeline is an E2E solution for time series forecasting task. AutoTSEstimator will replace original TSPipeline returned by AutoTSTrainer in later version. -.. automodule:: bigdl.chronos.autots.tspipeline - :members: - :undoc-members: - :show-inheritance: + +.. tabs:: + + .. tab:: PyTorch + + .. automodule:: bigdl.chronos.autots.tspipeline + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/readthedocs/source/doc/PythonAPI/Chronos/forecasters.rst b/docs/readthedocs/source/doc/PythonAPI/Chronos/forecasters.rst index d301835c..72eaeca9 100644 --- a/docs/readthedocs/source/doc/PythonAPI/Chronos/forecasters.rst +++ b/docs/readthedocs/source/doc/PythonAPI/Chronos/forecasters.rst @@ -8,23 +8,27 @@ Long short-term memory(LSTM) is a special type of recurrent neural network(RNN). For the detailed algorithm description, please refer to `here `__. -`version:pytorch` -:strong:`Please refer to` `BasePytorchForecaster `__ :strong:`for other methods other than initialization`. -.. automodule:: bigdl.chronos.forecaster.lstm_forecaster - :members: - :undoc-members: - :show-inheritance: +.. tabs:: -`version:tensorflow` + .. tab:: PyTorch -:strong:`Please refer to` `BaseTF2Forecaster `__ :strong:`for other methods other than initialization`. + .. automodule:: bigdl.chronos.forecaster.lstm_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + + + .. tab:: Tensorflow + + .. automodule:: bigdl.chronos.forecaster.tf.lstm_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: -.. automodule:: bigdl.chronos.forecaster.tf.lstm_forecaster - :members: - :undoc-members: - :show-inheritance: Seq2SeqForecaster @@ -32,23 +36,23 @@ Seq2SeqForecaster Seq2SeqForecaster wraps a sequence to sequence model based on LSTM, and is suitable for multivariant & multistep time series forecasting. -`version:pytorch` +.. tabs:: -:strong:`Please refer to` `BasePytorchForecaster `__ :strong:`for other methods other than initialization`. + .. tab:: PyTorch -.. automodule:: bigdl.chronos.forecaster.seq2seq_forecaster - :members: - :undoc-members: - :show-inheritance: + .. automodule:: bigdl.chronos.forecaster.seq2seq_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: -`version:tensorflow` + .. tab:: Tensorflow -:strong:`Please refer to` `BaseTF2Forecaster `__ :strong:`for other methods other than initialization`. - -.. automodule:: bigdl.chronos.forecaster.tf.seq2seq_forecaster - :members: - :undoc-members: - :show-inheritance: + .. automodule:: bigdl.chronos.forecaster.tf.seq2seq_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: TCNForecaster @@ -56,49 +60,55 @@ TCNForecaster Temporal Convolutional Networks (TCN) is a neural network that use convolutional architecture rather than recurrent networks. It supports multi-step and multi-variant cases. Causal Convolutions enables large scale parallel computing which makes TCN has less inference time than RNN based model such as LSTM. -`version:pytorch` +.. tabs:: -:strong:`Please refer to` `BasePytorchForecaster `__ :strong:`for other methods other than initialization`. + .. tab:: PyTorch -.. automodule:: bigdl.chronos.forecaster.tcn_forecaster - :members: - :undoc-members: - :show-inheritance: + .. automodule:: bigdl.chronos.forecaster.tcn_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: -`version:tensorflow` - -:strong:`Please refer to` `BaseTF2Forecaster `__ :strong:`for other methods other than initialization`. - -.. automodule:: bigdl.chronos.forecaster.tf.tcn_forecaster - :members: - :undoc-members: - :show-inheritance: + .. tab:: Tensorflow + .. automodule:: bigdl.chronos.forecaster.tf.tcn_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: AutoformerForecaster ---------------------------------------- Autoformer is a neural network that use transformer architecture with autocorrelation. It supports multi-step and multi-variant cases. It shows significant accuracy improvement while longer training/inference time than TCN. -`version:pytorch` +.. tabs:: -.. automodule:: bigdl.chronos.forecaster.autoformer_forecaster - :members: - :undoc-members: - :show-inheritance: + .. tab:: PyTorch + + .. automodule:: bigdl.chronos.forecaster.autoformer_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: NBeatsForecaster ---------------------------------------- -:strong:`Please refer to` `BasePytorchForecaster `__ :strong:`for other methods other than initialization`. -Neural basis expansion analysis for interpretable time series forecasting (`N-BEATS `__) is a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. Nbeats can solve univariate time series point forecasting problems, being interpretable, and fast to train. +.. tabs:: -.. automodule:: bigdl.chronos.forecaster.nbeats_forecaster - :members: - :undoc-members: - :show-inheritance: + .. tab:: PyTorch + + Neural basis expansion analysis for interpretable time series forecasting (`N-BEATS `__) is a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. Nbeats can solve univariate time series point forecasting problems, being interpretable, and fast to train. + + .. automodule:: bigdl.chronos.forecaster.nbeats_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: TCMFForecaster @@ -116,10 +126,16 @@ TCMFForecaster supports distributed training and inference. It is based on Orca * You can refer to `TCMFForecaster installation `__ to install required packages. * Your operating system (OS) is required to be one of the following 64-bit systems: **Ubuntu 16.04 or later** and **macOS 10.12.6 or later**. -.. automodule:: bigdl.chronos.forecaster.tcmf_forecaster - :members: - :undoc-members: - :show-inheritance: +.. tabs:: + + .. tab:: PyTorch + + .. automodule:: bigdl.chronos.forecaster.tcmf_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: + MTNetForecaster ---------------------------------------- @@ -130,10 +146,15 @@ MTNet is proposed by paper `A Memory-Network Based Solution for Multivariate Tim For the detailed algorithm description, please refer to `here `__. -.. automodule:: bigdl.chronos.forecaster.tf.mtnet_forecaster - :members: - :undoc-members: - :show-inheritance: +.. tabs:: + + .. tab:: Tensorflow + + .. automodule:: bigdl.chronos.forecaster.tf.mtnet_forecaster + :members: + :undoc-members: + :show-inheritance: + :inherited-members: ARIMAForecaster @@ -145,7 +166,7 @@ AutoRegressive Integrated Moving Average (ARIMA) is a class of statistical model :members: :undoc-members: :show-inheritance: - + :inherited-members: ProphetForecaster ---------------------------------------- @@ -158,20 +179,4 @@ For the detailed algorithm description, please refer to `here