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Chronos: Forecaster in doc shows more friendly (#3441)

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* add hyperlinks

* fix style error

* remove space

* remove typo "mtnet"
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liangs6212 2021-11-16 11:24:07 +08:00 committed by GitHub
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docs/readthedocs/source/doc/PythonAPI/Chronos/forecasters.rst
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@ -4,7 +4,7 @@ Forecasters
LSTMForecaster
----------------------------------------
Please refer to BasePytorchForecaster for other methods other than initialization.
:strong:`Please refer to` `BasePytorchForecaster <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html#basepytorchforecaster>`__ :strong:`for other methods other than initialization`.
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.
@ -19,7 +19,7 @@ For the detailed algorithm description, please refer to `here <https://github.co
Seq2SeqForecaster
-------------------------------------------
Please refer to BasePytorchForecaster for other methods other than initialization.
:strong:`Please refer to` `BasePytorchForecaster <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html#basepytorchforecaster>`__ :strong:`for other methods other than initialization`.
Seq2SeqForecaster wraps a sequence to sequence model based on LSTM, and is suitable for multivariant & multistep time series forecasting.
@ -32,7 +32,7 @@ Seq2SeqForecaster wraps a sequence to sequence model based on LSTM, and is suita
TCNForecaster
----------------------------------------
Please refer to BasePytorchForecaster for other methods other than initialization.
:strong:`Please refer to` `BasePytorchForecaster <https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Chronos/forecasters.html#basepytorchforecaster>`__ :strong:`for other methods other than initialization`.
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.