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Chronos: link updates for api docs (#3395)

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Junwei Deng 2021-11-04 15:09:10 +08:00 committed by GitHub
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docs/readthedocs/source/doc/PythonAPI/Chronos/forecasters.rst
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@ -8,7 +8,7 @@ Please refer to BasePytorchForecaster for other methods other than initializatio
Long short-term memory(LSTM) is a special type of recurrent neural network(RNN). We implement the basic version of LSTM - VanillaLSTM for this forecaster for time-series forecasting task. It has two LSTM layers, two dropout layer and a dense layer.
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/Chronos/Algorithm/LSTMAlgorithm.md>`__.
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/Chronos/Algorithm/LSTMAlgorithm.md>`__.
.. automodule:: bigdl.chronos.forecaster.lstm_forecaster
:members:
@ -45,7 +45,7 @@ Temporal Convolutional Networks (TCN) is a neural network that use convolutional
TCMFForecaster
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Analytics Zoo Chronos TCMFForecaster provides an efficient way to forecast high dimensional time series.
Chronos TCMFForecaster provides an efficient way to forecast high dimensional time series.
TCMFForecaster is based on DeepGLO algorithm, which is a deep forecasting model which thinks globally and acts locally.
You can refer to `the deepglo paper <https://arxiv.org/abs/1905.03806>`__ for more details.
@ -54,7 +54,7 @@ TCMFForecaster supports distributed training and inference. It is based on Orca
**Remarks**:
* You can refer to `TCMFForecaster installation <https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/Chronos/tutorials/TCMFForecaster.md/#step-0-prepare-environment>`__ to install required packages.
* You can refer to `TCMFForecaster installation <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/Chronos/tutorials/TCMFForecaster.md#step-0-prepare-environment>`__ 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
@ -68,9 +68,9 @@ MTNetForecaster
MTNet is a memory-network based solution for multivariate time-series forecasting. In a specific task of multivariate time-series forecasting, we have several variables observed in time series and we want to forecast some or all of the variables' value in a future time stamp.
MTNet is proposed by paper `A Memory-Network Based Solution for Multivariate Time-Series Forecasting <https://arxiv.org/abs/1809.02105>`__. MTNetForecaster is derived from tfpark.KerasMode, and can use all methods of KerasModel. Refer to `tfpark.KerasModel API Doc <https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/APIGuide/TFPark/model.md>`__ for details.
MTNet is proposed by paper `A Memory-Network Based Solution for Multivariate Time-Series Forecasting <https://arxiv.org/abs/1809.02105>`__. MTNetForecaster is derived from tfpark.KerasMode, and can use all methods of KerasModel. Refer to `tfpark.KerasModel API Doc <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/APIGuide/TFPark/model.md>`__ for details.
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/Chronos/Algorithm/MTNetAlgorithm.md>`__.
For the detailed algorithm description, please refer to `here <https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/Chronos/Algorithm/MTNetAlgorithm.md>`__.
.. automodule:: bigdl.chronos.forecaster.mtnet_forecaster
:members: