[DOC] improve and add tide model to docs (#1762)

### Description

Fixes #1758

Author: PranavBhatP

docs/source/models.rst

@@ -30,7 +30,7 @@ and you should take into account. Here is an overview over the pros and cons of
    :py:class:`~pytorch_forecasting.models.nhits.NHiTS`,                                                    "x",          "x",                "x",          "",               "",               "",           "",                            "",                        "",           1
    :py:class:`~pytorch_forecasting.models.deepar.DeepAR`,                                                  "x",          "x",                "x",          "",               "x",              "x",          "x [#deepvar]_ ",              "x",                       "",           3
    :py:class:`~pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer`,          "x",          "x",                "x",          "x",              "",               "x",          "",                            "x",                       "x",          4
-
+   :py:class:`~pytorch_forecasting.model.tide.TiDEModel`,                                                  "x",          "x",                "x",          "",               "",               "",           "",                            "x",                       "",           3
 
 .. [#deepvar] Accounting for correlations using a multivariate loss function which converts the network into a DeepVAR model.
 

pytorch_forecasting/__init__.py

@@ -46,6 +46,7 @@
     NHiTS,
     RecurrentNetwork,
     TemporalFusionTransformer,
+    TiDEModel,
     get_rnn,
 )
 from pytorch_forecasting.utils import (
@@ -70,6 +71,7 @@
     "NaNLabelEncoder",
     "MultiNormalizer",
     "TemporalFusionTransformer",
+    "TiDEModel",
     "NBeats",
     "NHiTS",
     "Baseline",

pytorch_forecasting/models/__init__.py

@@ -18,6 +18,7 @@
 from pytorch_forecasting.models.temporal_fusion_transformer import (
     TemporalFusionTransformer,
 )
+from pytorch_forecasting.models.tide import TiDEModel
 
 __all__ = [
     "NBeats",
@@ -35,4 +36,5 @@
     "GRU",
     "MultiEmbedding",
     "DecoderMLP",
+    "TiDEModel",
 ]

pytorch_forecasting/models/tide/_tide.py

@@ -1,3 +1,8 @@
+"""
+Implements the TiDE (Time-series Dense Encoder-decoder) model, which is designed for
+long-term time-series forecasting.
+"""
+
 from copy import copy
 from typing import Dict, List, Optional, Tuple, Union
 
@@ -44,30 +49,39 @@ def __init__(
     ):
         """An implementation of the TiDE model.
 
-        TiDE shares similarities with Transformers (implemented in :class:TransformerModel), but aims to deliver
-        better performance with reduced computational requirements by utilizing MLP-based encoder-decoder architectures
-        without attention mechanisms.
+        TiDE shares similarities with Transformers
+        (implemented in :class:TransformerModel), but aims to deliver better performance
+        with reduced computational requirements by utilizing MLP-based encoder-decoder
+        architectures without attention mechanisms.
 
-        This model supports future covariates (known for output_chunk_length points after the prediction time) and
-        static covariates.
+        This model supports future covariates (known for output_chunk_length points
+        after the prediction time) andstatic covariates.
 
-        The encoder and decoder are constructed using residual blocks. The number of residual blocks in the encoder and
-        decoder can be specified with `num_encoder_layers` and `num_decoder_layers` respectively. The layer width in the
-        residual blocks can be adjusted using `hidden_size`, while the layer width in the temporal decoder can be
-        controlled via `temporal_decoder_hidden`.
+        The encoder and decoder are constructed using residual blocks. The number of
+        residual blocks in the encoder and decoder can be specified with
+        `num_encoder_layers` and `num_decoder_layers` respectively. The layer width in
+        the residual blocks can be adjusted using `hidden_size`, while the layer width
+        in the temporal decoder can be controlled via `temporal_decoder_hidden`.
 
         Parameters
         ----------
-        input_chunk_length (int): Number of past time steps to use as input for the model (per chunk).
-            This applies to the target series and future covariates (if supported by the model).
-        output_chunk_length (int): Number of time steps the internal model predicts simultaneously (per chunk).
-            This also determines how many future values from future covariates are used as input
+        input_chunk_length :int
+            Number of past time steps to use as input for themodel (per chunk).
+            This applies to the target series and future covariates
             (if supported by the model).
-        num_encoder_layers (int): Number of residual blocks in the encoder. Defaults to 2.
-        num_decoder_layers (int): Number of residual blocks in the decoder. Defaults to 2.
-        decoder_output_dim (int): Dimensionality of the decoder's output. Defaults to 16.
-        hidden_size (int): Size of hidden layers in the encoder and decoder. Typically ranges from 32 to 128 when
-            no covariates are used. Defaults to 128.
+        output_chunk_length : int
+            Number of time steps the internal model predicts simultaneously (per chunk).
+            This also determines how many future values from future covariates
+            are used as input (if supported by the model).
+        num_encoder_layers : int, default=2
+            Number of residual blocks in the encoder
+        num_decoder_layers : int, default=2
+            Number of residual blocks in the decoder
+        decoder_output_dim : int, default=16
+            Dimensionality of the decoder's output
+        hidden_size : int, default=128
+            Size of hidden layers in the encoder and decoder.
+            Typically ranges from 32 to 128 when no covariates are used.
         temporal_width_future (int): Width of the output layer in the residual block for future covariate projections.
             If set to 0, bypasses feature projection and uses raw feature data. Defaults to 4.
         temporal_hidden_size_future (int): Width of the hidden layer in the residual block for future covariate
@@ -98,8 +112,10 @@ def __init__(
         **kwargs
             Allows optional arguments to configure pytorch_lightning.Module, pytorch_lightning.Trainer, and
             pytorch-forecasting's :class:BaseModelWithCovariates.
-        """  # noqa: E501
 
+        Note:
+            The model supports future covariates and static covariates.
+        """  # noqa: E501
         if static_categoricals is None:
             static_categoricals = []
         if static_reals is None:
@@ -200,15 +216,16 @@ def static_size(self) -> int:
     @classmethod
     def from_dataset(cls, dataset: TimeSeriesDataSet, **kwargs):
         """
-        Convenience function to create network from :py:class`~pytorch_forecasting.data.timeseries.TimeSeriesDataSet`.
+        Convenience function to create network from
+        :py:class`~pytorch_forecasting.data.timeseries.TimeSeriesDataSet`.
 
         Args:
             dataset (TimeSeriesDataSet): dataset where sole predictor is the target.
             **kwargs: additional arguments to be passed to `__init__` method.
 
         Returns:
             TiDE
-        """  # noqa: E501
+        """
 
         # validate arguments
         assert not isinstance(

pytorch_forecasting/models/tide/sub_modules.py

@@ -1,6 +1,6 @@
 """
 Time-series Dense Encoder (TiDE)
-------
+--------------------------------
 """
 
 from typing import Optional, Tuple
@@ -226,6 +226,7 @@ def forward(
         self, x_in: Tuple[torch.Tensor, Optional[torch.Tensor], Optional[torch.Tensor]]
     ) -> torch.Tensor:
         """TiDE model forward pass.
+
         Parameters
         ----------
         x_in