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Docs/general improvements #1904

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Aug 4, 2023
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Docs/general improvements #1904

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651d869
udpate dtw example and add dtw to rendered documentation
dennisbader Jul 18, 2023
ce1818e
Merge branch 'master' into fix/add_dtw_to_docs
dennisbader Jul 18, 2023
5601066
make all windows inherit from Window
dennisbader Jul 18, 2023
59e7d54
clean up windows
dennisbader Jul 18, 2023
aef3f2c
improve dtw documentation
dennisbader Jul 18, 2023
e9733b2
improved forecasting model module documentation
dennisbader Jul 18, 2023
b96a119
update models and add model links in covariates user guide
dennisbader Jul 18, 2023
ed2b893
add model links to README
dennisbader Jul 18, 2023
1229fa2
update changelog
dennisbader Jul 18, 2023
d711958
Merge branch 'master' into docs/general_improvements
dennisbader Jul 18, 2023
96f8232
fix typo in dtw example notebook
dennisbader Jul 18, 2023
fb7bca3
update with new tide model
dennisbader Jul 21, 2023
cea95c2
remove outdated lines from tide model from before probabilistic support
dennisbader Jul 21, 2023
865a20c
Merge branch 'master' into docs/general_improvements
madtoinou Jul 31, 2023
72d3f41
apply suggestions from PR review
dennisbader Jul 31, 2023
e9a8748
update readme model table
dennisbader Jul 31, 2023
08639b6
Feat/fit predict encodings (#1925)
dennisbader Jul 31, 2023
2bc1613
update readme
dennisbader Jul 31, 2023
59843c2
Merge branch 'master' into docs/general_improvements
dennisbader Aug 4, 2023
838032b
apply suggestions from code review and improve README.md
dennisbader Aug 4, 2023
93e6bbb
Update README.md
dennisbader Aug 4, 2023
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3 changes: 3 additions & 0 deletions CHANGELOG.md
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Expand Up @@ -24,6 +24,9 @@ but cannot always guarantee backwards compatibility. Changes that may **break co
- Improvements to `ShapExplainer`:
- Added static covariates support to `ShapeExplainer`. [#1803](https://github.com/unit8co/darts/pull/#1803) by [Anne de Vries](https://github.com/anne-devries) and [Dennis Bader](https://github.com/dennisbader).
- Improved static covariates column naming when applying a `sklearn.preprocessing.OneHotEncoder` with `StaticCovariatesTransformer` [#1863](https://github.com/unit8co/darts/pull/1863) by [Anne de Vries](https://github.com/anne-devries)
- Improvements to documentation [#1904](https://github.com/unit8co/darts/pull/#1904) by [Dennis Bader](https://github.com/dennisbader):
- made model sections in README.md, covariates user guide and forecasting model API Reference more user friendly by adding model links and reorganizing them into model categories.
- added the Dynamic Time Warping (DTW) module and improved its appearance.
- Added `MSTL` (Season-Trend decomposition using LOESS for multiple seasonalities) as a `method` option for `extract_trend_and_seasonality()`. [#1879](https://github.com/unit8co/darts/pull/1879) by [Alex Colpitts](https://github.com/alexcolpitts96)
- Added `RINorm` (Reversible Instance Norm) as a new layer normalization option. [#1121](https://github.com/unit8co/darts/issues/1121) by [Alex Colpitts](https://github.com/alexcolpitts96)
- New forecasting model: `TiDEModel` as proposed in [this paper](https://arxiv.org/abs/2304.08424). An MLP based encoder-decoder model that outperforms many Transformer-based architectures. [#1727](https://github.com/unit8co/darts/pull/1727) by [Alex Colpitts](https://github.com/alexcolpitts96).
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72 changes: 41 additions & 31 deletions README.md
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Expand Up @@ -212,37 +212,47 @@ on bringing more models and features.

Model | Univariate | Multivariate | Probabilistic | Multiple series (global) | Past-observed covariates | Future-known covariates | Static covariates | Reference
--- | --- | --- | --- | --- | --- | --- | --- | ---
`ARIMA` | ✅ | | ✅ | | | ✅ | |
`VARIMA` | | ✅ | | | | ✅ | |
`AutoARIMA` | ✅ | | | | | ✅ | |
`StatsForecastAutoARIMA` (faster AutoARIMA) | ✅ | | ✅ | | | ✅ | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
`ExponentialSmoothing` | ✅ | | ✅ | | | | |
`StatsForecastETS` | ✅ | | ✅ | | | ✅ | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
`StatsForecastAutoCES` | ✅ | | | | | | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
`BATS` and `TBATS` | ✅ | | ✅ | | | | | [TBATS paper](https://robjhyndman.com/papers/ComplexSeasonality.pdf)
`Theta` and `FourTheta` | ✅ | | | | | | | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R)
`StatsForecastAutoTheta` | ✅ | | ✅ | | | | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
`Prophet` (see [install notes](https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-support-for-facebook-prophet)) | ✅ | | ✅ | | | ✅ | | [Prophet repo](https://github.com/facebook/prophet)
`FFT` (Fast Fourier Transform) | ✅ | | | | | | |
`KalmanForecaster` using the Kalman filter and N4SID for system identification | ✅ | ✅ | ✅ | | | ✅ | | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf)
`Croston` method | ✅ | | | | | | |
`RegressionModel`; generic wrapper around any sklearn regression model | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ |
`RandomForest` | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ |
`LinearRegressionModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
`LightGBMModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
`CatBoostModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
`XGBModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
`RNNModel` (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | | ✅ | | [DeepAR paper](https://arxiv.org/abs/1704.04110)
`BlockRNNModel` (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | | |
`NBEATSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)
`NHiTSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
`TCNModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
`TransformerModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | |
`TFTModel` (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)
`DLinearModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [DLinear paper](https://arxiv.org/pdf/2205.13504.pdf)
`NLinearModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [NLinear paper](https://arxiv.org/pdf/2205.13504.pdf)
`TiDEModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TiDE paper](https://arxiv.org/pdf/2304.08424.pdf)
Naive Baselines | ✅ | ✅ | | | | | |
**Baseline Models** ([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms))| | | | | | | |
[NaiveMean](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMean) | ✅ | ✅ | | | | | |
[NaiveSeasonal](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveSeasonal) | ✅ | ✅ | | | | | |
[NaiveDrift](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveDrift) | ✅ | ✅ | | | | | |
[NaiveMovingAverage](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveMovingAverage) | ✅ | ✅ | | | | | |
**Statistical / Simpler Models** ([LocalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#local-forecasting-models-lfms)) | | | | | | | |
[ARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.arima.html#darts.models.forecasting.arima.ARIMA) | ✅ | | ✅ | | | ✅ | |
[VARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.varima.html#darts.models.forecasting.varima.VARIMA) | | ✅ | | | | ✅ | |
[AutoARIMA](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.auto_arima.html#darts.models.forecasting.auto_arima.AutoARIMA) | ✅ | | | | | ✅ | |
[StatsForecastAutoArima](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_arima.html#darts.models.forecasting.sf_auto_arima.StatsForecastAutoARIMA) (faster AutoARIMA) | ✅ | | ✅ | | | ✅ | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
[ExponentialSmoothing](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.exponential_smoothing.html#darts.models.forecasting.exponential_smoothing.ExponentialSmoothing) | ✅ | | ✅ | | | | |
[StatsforecastAutoETS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ets.html#darts.models.forecasting.sf_auto_ets.StatsForecastAutoETS) | ✅ | | ✅ | | | ✅ | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
[StatsforecastAutoCES](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_ces.html#darts.models.forecasting.sf_auto_ces.StatsForecastAutoCES) | ✅ | | | | | | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
[BATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.BATS) and [TBATS](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tbats_model.html#darts.models.forecasting.tbats_model.TBATS) | ✅ | | ✅ | | | | | [TBATS paper](https://robjhyndman.com/papers/ComplexSeasonality.pdf)
[Theta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.Theta) and [FourTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.theta.html#darts.models.forecasting.theta.FourTheta) | ✅ | | | | | | | [Theta](https://robjhyndman.com/papers/Theta.pdf) & [4 Theta](https://github.com/Mcompetitions/M4-methods/blob/master/4Theta%20method.R)
[StatsForecastAutoTheta](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.sf_auto_theta.html#darts.models.forecasting.sf_auto_theta.StatsForecastAutoTheta) | ✅ | | ✅ | | | | | [Nixtla's statsforecast](https://github.com/Nixtla/statsforecast)
[Prophet](file:///Users/dennisbader/projects/unit8/darts/docs/build/html/generated_api/darts.models.forecasting.prophet_model.html#darts.models.forecasting.prophet_model.Prophet) (see [install notes](https://github.com/unit8co/darts/blob/master/INSTALL.md#enabling-support-for-facebook-prophet)) | ✅ | | ✅ | | | ✅ | | [Prophet repo](https://github.com/facebook/prophet)
[FFT](file:///Users/dennisbader/projects/unit8/darts/docs/build/html/generated_api/darts.models.forecasting.fft.html#darts.models.forecasting.fft.FFT) (Fast Fourier Transform) | ✅ | | | | | | |
[KalmanForecaster](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.kalman_forecaster.html#darts.models.forecasting.kalman_forecaster.KalmanForecaster) using the Kalman filter and N4SID for system identification | ✅ | ✅ | ✅ | | | ✅ | | [N4SID paper](https://people.duke.edu/~hpgavin/SystemID/References/VanOverschee-Automatica-1994.pdf)
[Croston](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.croston.html#darts.models.forecasting.croston.Croston) method | ✅ | | | | | | |
**Regression Models** ([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms))| | | | | | | |
[RegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_model.html#darts.models.forecasting.regression_model.RegressionModel): generic wrapper around any sklearn regression model | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ |
[LinearRegressionModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.linear_regression_model.html#darts.models.forecasting.linear_regression_model.LinearRegressionModel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
[RandomForest](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.random_forest.html#darts.models.forecasting.random_forest.RandomForest) | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ |
[LightGBMModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.lgbm.html#darts.models.forecasting.lgbm.LightGBMModel), | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
[XGBModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.xgboost.html#darts.models.forecasting.xgboost.XGBModel)| ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
[CatBoostModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.catboost_model.html#darts.models.forecasting.catboost_model.CatBoostModel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
**PyTorch (Lightning)-based Models** ([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms))| | | | | | | |
[RNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.rnn_model.html#darts.models.forecasting.rnn_model.RNNModel) (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | | ✅ | | [DeepAR paper](https://arxiv.org/abs/1704.04110)
[BlockRNNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.block_rnn_model.html#darts.models.forecasting.block_rnn_model.BlockRNNModel) (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | | |
[NBEATSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nbeats.html#darts.models.forecasting.nbeats.NBEATSModel) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)
[NHiTSModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nhits.html#darts.models.forecasting.nhits.NHiTSModel) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [N-HiTS paper](https://arxiv.org/abs/2201.12886)
[TCNModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tcn_model.html#darts.models.forecasting.tcn_model.TCNModel) | ✅ | ✅ | ✅ | ✅ | ✅ | | | [TCN paper](https://arxiv.org/abs/1803.01271), [DeepTCN paper](https://arxiv.org/abs/1906.04397), [blog post](https://medium.com/unit8-machine-learning-publication/temporal-convolutional-networks-and-forecasting-5ce1b6e97ce4)
[TransformerModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.transformer_model.html#darts.models.forecasting.transformer_model.TransformerModel) | ✅ | ✅ | ✅ | ✅ | ✅ | | |
[TFTModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#darts.models.forecasting.tft_model.TFTModel) (Temporal Fusion Transformer) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TFT paper](https://arxiv.org/pdf/1912.09363.pdf), [PyTorch Forecasting](https://pytorch-forecasting.readthedocs.io/en/latest/models.html)
[DLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.dlinear.html#darts.models.forecasting.dlinear.DLinearModel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [DLinear paper](https://arxiv.org/pdf/2205.13504.pdf)
[NLinearModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.nlinear.html#darts.models.forecasting.nlinear.NLinearModel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [NLinear paper](https://arxiv.org/pdf/2205.13504.pdf)
[TiDEModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tide_model.html#darts.models.forecasting.tide_model.TiDEModel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | [TiDE paper](https://arxiv.org/pdf/2304.08424.pdf)
**Ensemble Models** ([GlobalForecastingModel](https://unit8co.github.io/darts/userguide/covariates.html#global-forecasting-models-gfms)): Model support is dependent on ensembled forecasting models and the ensemble model itself | | | | | | | |
[NaiveEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.baselines.html#darts.models.forecasting.baselines.NaiveEnsembleModel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
[RegressionEnsembleModel](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.regression_ensemble_model.html#darts.models.forecasting.regression_ensemble_model.RegressionEnsembleModel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |


## Community & Contact
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5 changes: 5 additions & 0 deletions darts/dataprocessing/dtw/__init__.py
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"""
Dynamic Time Warping (DTW)
--------------------------
"""

from .cost_matrix import CostMatrix
from .dtw import DTWAlignment, dtw
from .window import CRWindow, Itakura, NoWindow, SakoeChiba, Window
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