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Release 0.21.0 (#1149)
* bump u8darts version * update changelog * add Catboost model to list of models
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CHANGELOG.md

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Darts is still in an early development phase and we cannot always guarantee backwards compatibility. Changes that may **break code which uses a previous release of Darts** are marked with a "🔴".
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## [Unreleased](https://github.com/unit8co/darts/tree/master)
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- An issue where num_stacks is used instead of self.num_stacks in the NBEATSModel. Also, a few mistakes in API reference docs [#1103](https://github.com/unit8co/darts/pull/1103) by [Rijk van der Meulen](https://github.com/rijkvandermeulen)
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- Implemented the min_train_series_length method for the FourTheta and Theta models that overwrites the minimum default of 3 training samples by 2*seasonal_period when appropriate [#1101](https://github.com/unit8co/darts/pull/1101) by [Rijk van der Meulen](https://github.com/rijkvandermeulen)
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[Full Changelog](https://github.com/unit8co/darts/compare/0.20.0...master)
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[Full Changelog](https://github.com/unit8co/darts/compare/0.21.0...master)
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## [0.21.0](https://github.com/unit8co/darts/tree/0.21.0) (2022-08-12)
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### For users of the library:
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**Improved**
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- New model: Catboost, incl `quantile`, `poisson` and `gaussian` likelihoods support. [#1007](https://github.com/unit8co/darts/pull/1007), [#1044](https://github.com/unit8co/darts/pull/1044) by [Jonas Racine](https://github.com/jonasracine).
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- Extension of the `add_encoders` option to `RegressionModel`s. It is now straightforward to add calendar based or custom past or future covariates to these models, similar to torch models. [#1093](https://github.com/unit8co/darts/pull/1093) by [Dennis Bader](https://github.com/dennisbader).
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- Introduction of `StaticCovariatesTransformer`, categorical static covariate support for `TFTModel`, example and user-guide updates on static covariates. [#1081](https://github.com/unit8co/darts/pull/1081) by [Dennis Bader](https://github.com/dennisbader).
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- ARIMA and VARIMA models now support being applied to a new series, different than the one used for training. [#1036](https://github.com/unit8co/darts/pull/1036) by [Samuele Giuliano Piazzetta](https://github.com/piaz97).
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- All Darts forecasting models now have unified `save()` and `load()` methods. [#1070](https://github.com/unit8co/darts/pull/1070) by [Dustin Brunner](https://github.com/brunnedu).
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- Improvements in logging. [#1034](https://github.com/unit8co/darts/pull/1034) by [Dustin Brunner](https://github.com/brunnedu).
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- Re-integrating Prophet >= 1.1 in core dependencies (as it does not depend on PyStan anymore). [#1054](https://github.com/unit8co/darts/pull/1054) by [Julien Herzen](https://github.com/hrzn).
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- Added a new `AustralianTourismDataset`. [#1141](https://github.com/unit8co/darts/pull/1141) by [Julien Herzen](https://github.com/hrzn).
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- Added a new notebook demonstrating hierarchical reconciliation. [#1147](https://github.com/unit8co/darts/pull/1147) by [Julien Herzen](https://github.com/hrzn).
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- Added `drop_columns()` method to `TimeSeries`. [#1040](https://github.com/unit8co/darts/pull/1040) by [@shaido987](https://github.com/shaido987)
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- Speedup static covariates when no casting is needed. [#1053](https://github.com/unit8co/darts/pull/1053) by [Julien Herzen](https://github.com/hrzn).
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- Implemented the min_train_series_length method for the FourTheta and Theta models that overwrites the minimum default of 3 training samples by 2*seasonal_period when appropriate. [#1101](https://github.com/unit8co/darts/pull/1101) by [Rijk van der Meulen](https://github.com/rijkvandermeulen).
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- Make default formatting optional in plots. [#1056](https://github.com/unit8co/darts/pull/1056) by [Colin Delahunty](https://github.com/colin99d)
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- Introduce `retrain` option in `residuals()` method. [#1066](https://github.com/unit8co/darts/pull/1066) by [Julien Herzen](https://github.com/hrzn).
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- Improved error messages. [#1066](https://github.com/unit8co/darts/pull/1066) by [Julien Herzen](https://github.com/hrzn).
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- Small readability improvements to user guide. [#1039](https://github.com/unit8co/darts/pull/1039), [#1046](https://github.com/unit8co/darts/pull/1046/files) by [Ryan Russell](https://github.com/ryanrussell)
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**Fixed**
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- Fixed an error when loading torch forecasting models. [#1124](https://github.com/unit8co/darts/pull/1124) by [Dennis Bader](https://github.com/dennisbader).
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- 🔴 renamed `ignore_time_axes` into `ignore_time_axis` in `TimeSeries.concatenate()`. [#1073](https://github.com/unit8co/darts/pull/1073/files) by [Thomas KIENTZ](https://github.com/thomktz)
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- Propagate static covs and hierarchy in missing value filler. [#1076](https://github.com/unit8co/darts/pull/1076) by [Julien Herzen](https://github.com/hrzn).
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- Fixed an issue where num_stacks is used instead of self.num_stacks in the NBEATSModel. Also, a few mistakes in API reference docs. [#1103](https://github.com/unit8co/darts/pull/1103) by [Rijk van der Meulen](https://github.com/rijkvandermeulen).
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- Fixed `univariate_component()` method to propagate static covariates and drop hierarchy. [#1128](https://github.com/unit8co/darts/pull/1128) by [Julien Herzen](https://github.com/hrzn).
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- Fixed various issues. [#1106](https://github.com/unit8co/darts/pull/1106) by [Julien Herzen](https://github.com/hrzn).
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- Fixed an issue with `residuals` on `RNNModel`. [#1066](https://github.com/unit8co/darts/pull/1066) by [Julien Herzen](https://github.com/hrzn).
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## [0.20.0](https://github.com/unit8co/darts/tree/0.20.0) (2022-06-22)
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### For users of the library:

README.md

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`RandomForest` | ✅ | ✅ | | ✅ | ✅ | ✅ | |
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`LinearRegressionModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
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`LightGBMModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
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`CatBoostModel` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
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`RNNModel` (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version | ✅ | ✅ | ✅ | ✅ | | ✅ | | [DeepAR paper](https://arxiv.org/abs/1704.04110)
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`BlockRNNModel` (incl. LSTM and GRU) | ✅ | ✅ | ✅ | ✅ | ✅ | | |
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`NBEATSModel` | ✅ | ✅ | ✅ | ✅ | ✅ | | | [N-BEATS paper](https://arxiv.org/abs/1905.10437)

setup_u8darts.py

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setup(
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name="u8darts",
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version="0.20.0",
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version="0.21.0",
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description="A python library for easy manipulation and forecasting of time series.",
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long_description=LONG_DESCRIPTION,
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long_description_content_type="text/markdown",

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