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‎CHANGELOG.md

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# Release Notes
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## v1.4.0
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Feature and maintenance update.
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### Highlights
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* beta: experimental unified API for `pytorch-forecasting 2.0` release: [https://github.com/sktime/pytorch-forecasting/blob/main/docs/source/tutorials/ptf_V2_example.ipynb](notebook). Feedback appreciated in [issue 1736](https://github.com/sktime/pytorch-forecasting/issues/1736).
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* `TimeXer` model from `thuml` by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1797
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### Enhancements
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* [ENH] Add Type hints to `TimeSeriesDataSet` to align with pep 585 by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1819
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* [ENH] Allow multiple instances from multiple mock classes in `_safe_import` by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1818
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* [ENH] EXPERIMENTAL PR: D1 and D2 layer for v2 refactor by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1811
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* [ENH] EXPERIMENTAL PR: make the `data_module` dataclass-like by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1832
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* [ENH] EXPERIMENTAL: TFT model based on the new data pipeline by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1812
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* [ENH] test suite for `pytorch-forecasting` forecasters by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1780
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* [ENH] `TemporalFusionTransformer` - allow mixed precision training by @Marcrb2 in https://github.com/sktime/pytorch-forecasting/pull/1518
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* [ENH] move model base classes into `models.base` module - part 1 by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1773
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* [ENH] move model base classes into `models.base` module - part 2 by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1774
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* [ENH] move model base classes into `models.base` module - part 2 by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1776
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* [ENH] tests for `TiDE` Model by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1843
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* [ENH] refactor test metadata container to include data loader configs by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1861
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* [ENH] `DecoderMLP` metadata container for v1 tests by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1859
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* [ENH] `TimeXer` model from `thuml` by @PranavBhatP in https://github.com/sktime/pytorch-forecasting/pull/1797
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* [ENH] EXPERIMENTAL: Example notebook based on the new data pipeline by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1813
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* [ENH] refactor test data scenario generation to `tests._data_scenarios` by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1877
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### Fixes
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* [BUG] fix absolute errorbar by @MartinoMensio in https://github.com/sktime/pytorch-forecasting/pull/1579
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* [BUG] EXPERIMENTAL PR: Solve the bug in `data_module` by @phoeenniixx in https://github.com/sktime/pytorch-forecasting/pull/1834
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* [BUG] fix incorrect concatenation dimension in `concat_sequences` by @cngmid in https://github.com/sktime/pytorch-forecasting/pull/1827
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* [BUG] Fix for the case when reduction is set to `none` by @fnhirwa in https://github.com/sktime/pytorch-forecasting/pull/1872
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* [BUG] enable silenced TFT v2 tests by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1878
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### Documentation
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* [DOC] fix `gradient_clip` value in tutorials to ensure reproducible outputs similar to the committed cell output by @gbilleyPeco in https://github.com/sktime/pytorch-forecasting/pull/1750
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* [DOC] Fix typos in getting started section of the documentation by @pietsjoh in https://github.com/sktime/pytorch-forecasting/pull/1399
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* [DOC] improved pull request template by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1866
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* [DOC] add project badges to README: sponsoring and downloads by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1891
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### Maintenance
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* [MNT] Isolate `cpflow` package, towards fixing readthedocs build by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1775
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* [MNT] fix readthedocs build by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1777
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* [MNT] move release to trusted publishers by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1800
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* [MNT] standardize `dependabot.yml` by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1799
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* [MNT] remove `tj-actions` by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1798
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* [MNT] [Dependabot](deps): bump codecov/codecov-action from 1 to 5 by @dependabot in https://github.com/sktime/pytorch-forecasting/pull/1803
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* [MNT] disable automated merge and approve actions by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1804
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* build(deps): update sphinx requirement from `<7.2.6,>3.2` to `>3.2,<8.2.4` by @dependabot in https://github.com/sktime/pytorch-forecasting/pull/1787
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* [MNT] Move config from `setup.cfg` to `pyproject.toml` by @Borda in https://github.com/sktime/pytorch-forecasting/pull/1852
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* [MNT] Move `pytest` configuration to `pyproject.toml` by @Borda in https://github.com/sktime/pytorch-forecasting/pull/1851
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* [MNT] Add 'UP' to extend-select for pyupgrade python syntax by @Borda in https://github.com/sktime/pytorch-forecasting/pull/1856
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* [MNT] Replace Black with Ruff formatting and update configuration by @Borda in https://github.com/sktime/pytorch-forecasting/pull/1853
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* [MNT] issue templates by @fkiraly in https://github.com/sktime/pytorch-forecasting/pull/1867
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* [MNT] Clearly define the MLP as a class/nn.model by @jobs-git in https://github.com/sktime/pytorch-forecasting/pull/1864
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### All Contributors
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@agobbifbk,
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@Borda,
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@cngmid,
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@fkiraly,
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@fnhirwa,
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@gbilleyPeco,
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@jobs-git,
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@Marcrb2,
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@MartinoMensio,
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@phoeenniixx,
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@pietsjoh,
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@PranavBhatP
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## v1.3.0
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Feature and maintenance update.

‎pyproject.toml

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[project]
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name = "pytorch-forecasting"
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readme = "README.md" # Markdown files are supported
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version = "1.3.0" # is being replaced automatically
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version = "1.4.0" # is being replaced automatically
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authors = [
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{name = "Jan Beitner"},

‎pytorch_forecasting/__init__.py

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PyTorch Forecasting package for timeseries forecasting with PyTorch.
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"""
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__version__ = "1.3.0"
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__version__ = "1.4.0"
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from pytorch_forecasting.data import (
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EncoderNormalizer,

‎pytorch_forecasting/models/base/_base_object.py

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class _BasePtForecaster(_BaseObject):
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"""Base class for all PyTorch Forecasting forecaster metadata.
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"""Base class for all PyTorch Forecasting forecaster packages.
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This class points to model objects and contains metadata as tags.
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"""

‎pytorch_forecasting/models/deepar/__init__.py

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"""DeepAR: Probabilistic forecasting with autoregressive recurrent networks."""
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from pytorch_forecasting.models.deepar._deepar import DeepAR
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from pytorch_forecasting.models.deepar._deepar_metadata import DeepARMetadata
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from pytorch_forecasting.models.deepar._deepar_pkg import DeepAR_pkg
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__all__ = ["DeepAR", "DeepARMetadata"]
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__all__ = ["DeepAR", "DeepAR_pkg"]

‎pytorch_forecasting/models/deepar/_deepar.py

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class DeepAR(AutoRegressiveBaseModelWithCovariates):
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"""DeepAR: Probabilistic forecasting with autoregressive recurrent networks."""
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def __init__(
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self,
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cell_type: str = "LSTM",

‎pytorch_forecasting/models/deepar/_deepar_metadata.py renamed to ‎pytorch_forecasting/models/deepar/_deepar_pkg.py

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"""DeepAR metadata container."""
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"""DeepAR package container."""
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from pytorch_forecasting.models.base._base_object import _BasePtForecaster
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class DeepARMetadata(_BasePtForecaster):
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"""DeepAR metadata container."""
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class DeepAR_pkg(_BasePtForecaster):
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"""DeepAR package container."""
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_tags = {
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"info:name": "DeepAR",

‎pytorch_forecasting/models/mlp/__init__.py

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"""Simple models based on fully connected networks."""
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from pytorch_forecasting.models.mlp._decodermlp import DecoderMLP
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from pytorch_forecasting.models.mlp._decodermlp_metadata import DecoderMLPMetadata
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from pytorch_forecasting.models.mlp._decodermlp_pkg import DecoderMLP_pkg
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from pytorch_forecasting.models.mlp.submodules import FullyConnectedModule
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__all__ = ["DecoderMLP", "DecoderMLPMetadata", "FullyConnectedModule"]
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__all__ = ["DecoderMLP", "DecoderMLP_pkg", "FullyConnectedModule"]

‎pytorch_forecasting/models/mlp/_decodermlp.py

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class DecoderMLP(BaseModelWithCovariates):
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"""
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MLP on the decoder.
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"""MLP on the decoder.
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MLP that predicts output only based on information available in the decoder.
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"""

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"""DecoderMLP metadata container."""
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"""DecoderMLP package container."""
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from pytorch_forecasting.models.base._base_object import _BasePtForecaster
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class DecoderMLPMetadata(_BasePtForecaster):
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"""DecoderMLP metadata container."""
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class DecoderMLP_pkg(_BasePtForecaster):
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"""DecoderMLP package container."""
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‎pytorch_forecasting/models/nbeats/__init__.py

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"""N-Beats model for timeseries forecasting without covariates."""
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from pytorch_forecasting.models.nbeats._nbeats import NBeats
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from pytorch_forecasting.models.nbeats._nbeats_pkg import NBeats_pkg
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"NBeats_pkg",
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"NBEATSTrendBlock",
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class NBeats(BaseModel):
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"""N-Beats model for timeseries forecasting without covariates."""
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"""NBeats metadata container."""
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"""NBeats package container."""
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class NBeatsMetadata(_BasePtForecaster):
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"""NBeats metadata container."""
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class NBeats_pkg(_BasePtForecaster):
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"""NBeats package container."""
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‎pytorch_forecasting/models/tide/__init__.py

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"""Tide model."""
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from pytorch_forecasting.models.tide._tide import TiDEModel
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from pytorch_forecasting.models.tide._tide_pkg import TiDEModel_pkg
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__all__ = [
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"_TideModule",
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"TiDEModel",
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"TiDEModel_pkg",
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‎pytorch_forecasting/models/tide/_tide.py

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class TiDEModel(BaseModelWithCovariates):
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"""TiDE model for long-term time-series forecasting."""
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"""TiDE package container."""
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class TiDEModelMetadata(_BasePtForecaster):
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"""Metadata container for TiDE Model."""
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class TiDEModel_pkg(_BasePtForecaster):
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"""Package container for TiDE Model."""
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‎pytorch_forecasting/models/timexer/__init__.py

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"""
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from pytorch_forecasting.models.timexer._timexer import TimeXer
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from pytorch_forecasting.models.timexer._timexer_pkg import TimeXer_pkg
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from pytorch_forecasting.models.timexer.sub_modules import (
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"EnEmbedding",
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"Encoder",
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"EncoderLayer",
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"TimeXer_pkg",
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]

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