Etna

Added
- `DeseasonalityTransform` ([1307](https://github.com/tinkoff-ai/etna/pull/1307))
- Add extension with models from `statsforecast`: `StatsForecastARIMAModel`, `StatsForecastAutoARIMAModel`, `StatsForecastAutoCESModel`, `StatsForecastAutoETSModel`, `StatsForecastAutoThetaModel` ([1295](https://github.com/tinkoff-ai/etna/pull/1297))
- Notebook `feature_selection` ([875](https://github.com/tinkoff-ai/etna/pull/875))
- Implementation of PatchTS model ([1277](https://github.com/tinkoff-ai/etna/pull/1277))

Changed
- Add modes `binary` and `category` to `HolidayTransform` ([763](https://github.com/tinkoff-ai/etna/pull/763))
- Add sorting by timestamp before the fit in `CatBoostPerSegmentModel` and `CatBoostMultiSegmentModel` ([1337](https://github.com/tinkoff-ai/etna/pull/1337))
- Speed up metrics computation by optimizing segment validation, forbid NaNs during metrics computation ([1338](https://github.com/tinkoff-ai/etna/pull/1338))
- Unify errors, warnings and checks in models ([1312](https://github.com/tinkoff-ai/etna/pull/1312))
- Remove upper limitation on version of numba ([1321](https://github.com/tinkoff-ai/etna/pull/1321))
- Optimize `TSDataset.describe` and `TSDataset.info` by vectorization ([1344](https://github.com/tinkoff-ai/etna/pull/1344))
- Add documentation warning about using dill during loading ([1346](https://github.com/tinkoff-ai/etna/pull/1346))
- Vectorize metric computation ([1347](https://github.com/tinkoff-ai/etna/pull/1347))

Fixed
- Pipeline ensembles fail in `etna forecast` CLI ([1331](https://github.com/tinkoff-ai/etna/pull/1331))
- Fix performance of `DeepARModel` and `TFTModel` ([1322](https://github.com/tinkoff-ai/etna/pull/1322))
- `mrmr` feature selection working with categoricals ([1311](https://github.com/tinkoff-ai/etna/pull/1311))
- Fix version of `statsforecast` to 1.4 to avoid dependency conflicts during installation ([1313](https://github.com/tinkoff-ai/etna/pull/1313))
- Add inverse transformation into `predict` method of pipelines ([1314](https://github.com/tinkoff-ai/etna/pull/1314))
- Allow saving large pipelines ([1335](https://github.com/tinkoff-ai/etna/pull/1335))
- Fix link for dataset in classification notebook ([1351](https://github.com/tinkoff-ai/etna/pull/1351))

Removed
- Building docker images with cuda 10.2 ([1306](https://github.com/tinkoff-ai/etna/pull/1306))

Added
- Notebook `forecast_interpretation.ipynb` with forecast decomposition ([1220](https://github.com/tinkoff-ai/etna/pull/1220))
- Exogenous variables shift transform `ExogShiftTransform`([1254](https://github.com/tinkoff-ai/etna/pull/1254))
- Parameter `start_timestamp` to forecast CLI command ([1265](https://github.com/tinkoff-ai/etna/pull/1265))
- `DeepStateModel` ([1253](https://github.com/tinkoff-ai/etna/pull/1253))
- `NBeatsGenericModel` and `NBeatsInterpretableModel` ([1302](https://github.com/tinkoff-ai/etna/pull/1302))
- Function `estimate_max_n_folds` for folds number estimation ([1279](https://github.com/tinkoff-ai/etna/pull/1279))
- Parameters `estimate_n_folds` and `context_size` to forecast and backtest CLI commands ([1284](https://github.com/tinkoff-ai/etna/pull/1284))
- Class `Tune` for hyperparameter optimization within existing pipeline ([1200](https://github.com/tinkoff-ai/etna/pull/1200))
- Add `etna.distributions` for using it instead of using `optuna.distributions` ([1292](https://github.com/tinkoff-ai/etna/pull/1292))

Changed
- Set the default value of `final_model` to `LinearRegression(positive=True)` in the constructor of `StackingEnsemble` ([1238](https://github.com/tinkoff-ai/etna/pull/1238))
- Add microseconds to `FileLogger`'s directory name ([1264](https://github.com/tinkoff-ai/etna/pull/1264))
- Inherit `SaveMixin` from `AbstractSaveable` for mypy checker ([1261](https://github.com/tinkoff-ai/etna/pull/1261))
- Update requirements for `holidays` and `scipy`, change saving library from `pickle` to `dill` in `SaveMixin` ([1268](https://github.com/tinkoff-ai/etna/pull/1268))
- Update requirement for `ruptures`, add requirement for `sqlalchemy` ([1276](https://github.com/tinkoff-ai/etna/pull/1276))
- Optimize `make_samples` of `RNNNet` and `MLPNet` ([1281](https://github.com/tinkoff-ai/etna/pull/1281))
- Remove `to_be_fixed` from inference tests on `SpecialDaysTransform` ([1283](https://github.com/tinkoff-ai/etna/pull/1283))
- Rewrite `TimeSeriesImputerTransform` to work without per-segment wrapper ([1293](https://github.com/tinkoff-ai/etna/pull/1293))
- Add default `params_to_tune` for catboost models ([1185](https://github.com/tinkoff-ai/etna/pull/1185))
- Add default `params_to_tune` for `ProphetModel` ([1203](https://github.com/tinkoff-ai/etna/pull/1203))
- Add default `params_to_tune` for `SARIMAXModel`, change default parameters for the model ([1206](https://github.com/tinkoff-ai/etna/pull/1206))
- Add default `params_to_tune` for linear models ([1204](https://github.com/tinkoff-ai/etna/pull/1204))
- Add default `params_to_tune` for `SeasonalMovingAverageModel`, `MovingAverageModel`, `NaiveModel` and `DeadlineMovingAverageModel` ([1208](https://github.com/tinkoff-ai/etna/pull/1208))
- Add default `params_to_tune` for `DeepARModel` and `TFTModel` ([1210](https://github.com/tinkoff-ai/etna/pull/1210))
- Add default `params_to_tune` for `HoltWintersModel`, `HoltModel` and `SimpleExpSmoothingModel` ([1209](https://github.com/tinkoff-ai/etna/pull/1209))
- Add default `params_to_tune` for `RNNModel` and `MLPModel` ([1218](https://github.com/tinkoff-ai/etna/pull/1218))
- Add default `params_to_tune` for `DateFlagsTransform`, `TimeFlagsTransform`, `SpecialDaysTransform` and `FourierTransform` ([1228](https://github.com/tinkoff-ai/etna/pull/1228))
- Add default `params_to_tune` for `MedianOutliersTransform`, `DensityOutliersTransform` and `PredictionIntervalOutliersTransform` ([1231](https://github.com/tinkoff-ai/etna/pull/1231))
- Add default `params_to_tune` for `TimeSeriesImputerTransform` ([1232](https://github.com/tinkoff-ai/etna/pull/1232))
- Add default `params_to_tune` for `DifferencingTransform`, `MedianTransform`, `MaxTransform`, `MinTransform`, `QuantileTransform`, `StdTransform`, `MeanTransform`, `MADTransform`, `MinMaxDifferenceTransform`, `SumTransform`, `BoxCoxTransform`, `YeoJohnsonTransform`, `MaxAbsScalerTransform`, `MinMaxScalerTransform`, `RobustScalerTransform` and `StandardScalerTransform` ([1233](https://github.com/tinkoff-ai/etna/pull/1233))
- Add default `params_to_tune` for `LabelEncoderTransform` ([1242](https://github.com/tinkoff-ai/etna/pull/1242))
- Add default `params_to_tune` for `ChangePointsSegmentationTransform`, `ChangePointsTrendTransform`, `ChangePointsLevelTransform`, `TrendTransform`, `LinearTrendTransform`, `TheilSenTrendTransform` and `STLTransform` ([1243](https://github.com/tinkoff-ai/etna/pull/1243))
- Add default `params_to_tune` for `TreeFeatureSelectionTransform`, `MRMRFeatureSelectionTransform` and `GaleShapleyFeatureSelectionTransform` ([1250](https://github.com/tinkoff-ai/etna/pull/1250))
- Add tuning stage into `Auto.fit` ([1272](https://github.com/tinkoff-ai/etna/pull/1272))
- Add `params_to_tune` into `Tune` init ([1282](https://github.com/tinkoff-ai/etna/pull/1282))
- Skip duplicates during `Tune.fit`, skip duplicates in `top_k`, add AutoML notebook ([1285](https://github.com/tinkoff-ai/etna/pull/1285))
- Add parameter `fast_redundancy` in `mrmm`, fix relevance calculation in `get_model_relevance_table` ([1294](https://github.com/tinkoff-ai/etna/pull/1294))

Fixed
- Fix `plot_backtest` and `plot_backtest_interactive` on one-step forecast ([1260](https://github.com/tinkoff-ai/etna/pull/1260))
- Fix `BaseReconciliator` to work on `pandas==1.1.5` ([1229](https://github.com/tinkoff-ai/etna/pull/1229))
- Fix `TSDataset.make_future` to handle hierarchy, quantiles, target components ([1248](https://github.com/tinkoff-ai/etna/pull/1248))
- Fix warning during creation of `ResampleWithDistributionTransform` ([1230](https://github.com/tinkoff-ai/etna/pull/1230))
- Add deep copy for copying attributes of `TSDataset` ([1241](https://github.com/tinkoff-ai/etna/pull/1241))
- Add `tsfresh` into optional dependencies, remove instruction about `pip install tsfresh` ([1246](https://github.com/tinkoff-ai/etna/pull/1246))
- Fix `DeepARModel` and `TFTModel` to work with changed `prediction_size` ([1251](https://github.com/tinkoff-ai/etna/pull/1251))
- Fix problems with flake8 B023 ([1252](https://github.com/tinkoff-ai/etna/pull/1252))
- Fix problem with swapped forecast methods in HierarchicalPipeline ([1259](https://github.com/tinkoff-ai/etna/pull/1259))
- Fix problem with segment name "target" in `StackingEnsemble` ([1262](https://github.com/tinkoff-ai/etna/pull/1262))
- Fix `BasePipeline.forecast` when prediction intervals are estimated on history data with presence of NaNs ([1291](https://github.com/tinkoff-ai/etna/pull/1291))
- Teach `BaseMixin.set_params` to work with nested `list` and `tuple` ([1201](https://github.com/tinkoff-ai/etna/pull/1201))
- Fix `get_anomalies_prediction_interval` to work when segments have different start date ([1296](https://github.com/tinkoff-ai/etna/pull/1296))
- Fix `classification` notebook to download `FordA` dataset without error ([1299](https://github.com/tinkoff-ai/etna/pull/1299))
- Fix signature of `Auto.fit`, `Tune.fit` to not have a breaking change ([1300](https://github.com/tinkoff-ai/etna/pull/1300))

Added
- Target components logic into `AutoRegressivePipeline` ([1188](https://github.com/tinkoff-ai/etna/pull/1188))
- Target components logic into `HierarchicalPipeline` ([1199](https://github.com/tinkoff-ai/etna/pull/1199))
- `predict` method into `HierarchicalPipeline` ([1199](https://github.com/tinkoff-ai/etna/pull/1199))
- Add target components handling in `get_level_dataframe` ([1179](https://github.com/tinkoff-ai/etna/pull/1179))
- Forecast decomposition for `SeasonalMovingAverageModel`([1180](https://github.com/tinkoff-ai/etna/pull/1180))
- Target components logic into base classes of pipelines ([1173](https://github.com/tinkoff-ai/etna/pull/1173))
- Method `predict_components` for forecast decomposition in `_SklearnAdapter` and `_LinearAdapter` for linear models ([1164](https://github.com/tinkoff-ai/etna/pull/1164))
- Target components logic into base classes of models ([1158](https://github.com/tinkoff-ai/etna/pull/1158))
- Target components logic to TSDataset ([1153](https://github.com/tinkoff-ai/etna/pull/1153))
- Methods `save` and `load` to HierarchicalPipeline ([1096](https://github.com/tinkoff-ai/etna/pull/1096))
- New data access methods in `TSDataset` : `update_columns_from_pandas`, `add_columns_from_pandas`, `drop_features` ([809](https://github.com/tinkoff-ai/etna/pull/809))
- `PytorchForecastingDatasetBuiler` for neural networks from Pytorch Forecasting ([971](https://github.com/tinkoff-ai/etna/pull/971))
- New base classes for per-segment and multi-segment transforms `IrreversiblePersegmentWrapper`, `ReversiblePersegmentWrapper`, `IrreversibleTransform`, `ReversibleTransform` ([835](https://github.com/tinkoff-ai/etna/pull/835))
- New base class for one segment transforms `OneSegmentTransform` ([894](https://github.com/tinkoff-ai/etna/pull/894))
- `ChangePointsLevelTransform` and base classes `PerIntervalModel`, `BaseChangePointsModelAdapter` for per-interval transforms ([998](https://github.com/tinkoff-ai/etna/pull/998))
- Method `set_params` to change parameters of ETNA objects ([1102](https://github.com/tinkoff-ai/etna/pull/1102))
- Function `plot_forecast_decomposition` ([1129](https://github.com/tinkoff-ai/etna/pull/1129))
- Method `forecast_components` for forecast decomposition in `_TBATSAdapter` ([1133](https://github.com/tinkoff-ai/etna/pull/1133))
- Methods `forecast_components` and `predict_components` for forecast decomposition in `_CatBoostAdapter` ([1148](https://github.com/tinkoff-ai/etna/pull/1148))
- Methods `forecast_components` and `predict_components` for forecast decomposition in `_HoltWintersAdapter ` ([1162](https://github.com/tinkoff-ai/etna/pull/1162))
- Method `predict_components` for forecast decomposition in `_ProphetAdapter` ([1172](https://github.com/tinkoff-ai/etna/pull/1172))
- Methods `forecast_components` and `predict_components` for forecast decomposition in `_SARIMAXAdapter` and `_AutoARIMAAdapter` ([1174](https://github.com/tinkoff-ai/etna/pull/1174))
- Add `refit` parameter into `backtest` ([1159](https://github.com/tinkoff-ai/etna/pull/1159))
- Add `stride` parameter into `backtest` ([1165](https://github.com/tinkoff-ai/etna/pull/1165))
- Add optional parameter `ts` into `forecast` method of pipelines ([1071](https://github.com/tinkoff-ai/etna/pull/1071))
- Add tests on `transform` method of transforms on subset of segments, on new segments, on future with gap ([1094](https://github.com/tinkoff-ai/etna/pull/1094))
- Add tests on `inverse_transform` method of transforms on subset of segments, on new segments, on future with gap ([1127](https://github.com/tinkoff-ai/etna/pull/1127))
- In-sample prediction for `BATSModel` and `TBATSModel` ([1181](https://github.com/tinkoff-ai/etna/pull/1181))
- Method `predict_components` for forecast decomposition in `_TBATSAdapter` ([1181](https://github.com/tinkoff-ai/etna/pull/1181))
- Forecast decomposition for `DeadlineMovingAverageModel`([1186](https://github.com/tinkoff-ai/etna/pull/1186))
- Prediction decomposition example into `custom_transform_and_model.ipynb`([1216](https://github.com/tinkoff-ai/etna/pull/1216))

Changed
- Add optional `features` parameter in the signature of `TSDataset.to_pandas`, `TSDataset.to_flatten` ([809](https://github.com/tinkoff-ai/etna/pull/809))
- Signature of the constructor of `TFTModel`, `DeepARModel` ([1110](https://github.com/tinkoff-ai/etna/pull/1110))
- Interface of `Transform` and `PerSegmentWrapper` ([835](https://github.com/tinkoff-ai/etna/pull/835))
- Signature of `TSDataset` methods `inverse_transform` and `make_future` now has `transforms` parameter. Remove transforms and regressors updating logic from TSDataset. Forecasts from the models are not internally inverse transformed. Methods `fit`,`transform`,`inverse_transform` of `Transform` now works with `TSDataset` ([956](https://github.com/tinkoff-ai/etna/pull/956))
- Create `AutoBase` and `AutoAbstract` classes, some of `Auto` class's logic moved there ([1114](https://github.com/tinkoff-ai/etna/pull/1114))
- Impose specific order of columns on return value of `TSDataset.to_flatten` ([1095](https://github.com/tinkoff-ai/etna/pull/1095))
- Add more scenarios into tests for models ([1082](https://github.com/tinkoff-ai/etna/pull/1082))
- Decouple `SeasonalMovingAverageModel` from `PerSegmentModelMixin` ([1132](https://github.com/tinkoff-ai/etna/pull/1132))
- Decouple `DeadlineMovingAverageModel` from `PerSegmentModelMixin` ([1140](https://github.com/tinkoff-ai/etna/pull/1140))
- Remove version python-3.7 from `pyproject.toml`, update lock ([1183](https://github.com/tinkoff-ai/etna/pull/1183))
- Bump minimum pandas version up to 1.1 ([1214](https://github.com/tinkoff-ai/etna/pull/1214))

Fixed
- Fix bug in `GaleShapleyFeatureSelectionTransform` with wrong number of remaining features ([1110](https://github.com/tinkoff-ai/etna/pull/1110))
- `ProphetModel` fails with additional seasonality set ([1157](https://github.com/tinkoff-ai/etna/pull/1157))
- Fix inference tests on new segments for `DeepARModel` and `TFTModel` ([1109](https://github.com/tinkoff-ai/etna/pull/1109))
- Fix alignment during forecasting in new NNs, add validation of context size during forecasting in new NNs, add validation of batch in `MLPNet` ([1108](https://github.com/tinkoff-ai/etna/pull/1108))
- Fix `MeanSegmentEncoderTransform` to work with subset of segments and raise error on new segments ([1104](https://github.com/tinkoff-ai/etna/pull/1104))
- Fix outliers transforms on future with gap ([1147](https://github.com/tinkoff-ai/etna/pull/1147))
- Fix `SegmentEncoderTransform` to work with subset of segments and raise error on new segments ([1103](https://github.com/tinkoff-ai/etna/pull/1103))
- Fix `SklearnTransform` in per-segment mode to work on subset of segments and raise error on new segments ([1107](https://github.com/tinkoff-ai/etna/pull/1107))
- Fix `OutliersTransform` and its children to raise error on new segments ([1139](https://github.com/tinkoff-ai/etna/pull/1139))
- Fix `DifferencingTransform` to raise error on new segments during `transform` and `inverse_transform` in inplace mode ([1141](https://github.com/tinkoff-ai/etna/pull/1141))
- Teach `DifferencingTransform` to `inverse_transform` with NaNs ([1155](https://github.com/tinkoff-ai/etna/pull/1155))
- Fixed `custom_transform_and_model.ipynb`([1216](https://github.com/tinkoff-ai/etna/pull/1216))

Removed
- `sample_acf_plot`, `sample_pacf_plot`, `CatBoostModelPerSegment`, `CatBoostModelMultiSegment` ([1118](https://github.com/tinkoff-ai/etna/pull/1118))
- `PytorchForecastingTransform` ([971](https://github.com/tinkoff-ai/etna/pull/971))

Added
- `RMSE` metric & `rmse` functional metric ([1051](https://github.com/tinkoff-ai/etna/pull/1051))
- `MaxDeviation` metric & `max_deviation` functional metric ([1061](https://github.com/tinkoff-ai/etna/pull/1061))
- Add saving/loading for transforms, models, pipelines, ensembles; tutorial for saving/loading ([1068](https://github.com/tinkoff-ai/etna/pull/1068))
- Add hierarchical time series support([1083](https://github.com/tinkoff-ai/etna/pull/1083))
- Add `WAPE` metric & `wape` functional metric ([1085](https://github.com/tinkoff-ai/etna/pull/1085))

Fixed
- Missed kwargs in TFT init([1078](https://github.com/tinkoff-ai/etna/pull/1078))

Added
- Add python 3.10 support ([1005](https://github.com/tinkoff-ai/etna/pull/1005))
- Add `SumTranform`([1021](https://github.com/tinkoff-ai/etna/pull/1021))
- Add `plot_change_points_interactive` ([988](https://github.com/tinkoff-ai/etna/pull/988))
- Add `experimental` module with `TimeSeriesBinaryClassifier` and `PredictabilityAnalyzer` ([985](https://github.com/tinkoff-ai/etna/pull/985))
- Inference track results: add `predict` method to pipelines, teach some models to work with context, change hierarchy of base models, update notebook examples ([979](https://github.com/tinkoff-ai/etna/pull/979))
- Add `get_ruptures_regularization` into `experimental` module ([1001](https://github.com/tinkoff-ai/etna/pull/1001))
- Add example `classification` notebook for experimental classification feature ([997](https://github.com/tinkoff-ai/etna/pull/997))
Changed
- Change returned model in get_model of BATSModel, TBATSModel ([987](https://github.com/tinkoff-ai/etna/pull/987))
- Add acf_plot, deprecated sample_acf_plot, sample_pacf_plot ([1004](https://github.com/tinkoff-ai/etna/pull/1004))
- Change returned model in `get_model` of `HoltWintersModel`, `HoltModel`, `SimpleExpSmoothingModel` ([986](https://github.com/tinkoff-ai/etna/pull/986))
Fixed
- Fix `MinMaxDifferenceTransform` import ([1030](https://github.com/tinkoff-ai/etna/pull/1030))
- Fix release docs and docker images cron job ([982](https://github.com/tinkoff-ai/etna/pull/982))
- Fix forecast first point with CatBoostPerSegmentModel ([1010](https://github.com/tinkoff-ai/etna/pull/1010))
- Fix hanging EDA notebook ([1027](https://github.com/tinkoff-ai/etna/pull/1027))
- Fix hanging EDA notebook v2 + cache clean script ([1034](https://github.com/tinkoff-ai/etna/pull/1034))

Added
- Add `greater_is_better` property for Metric ([921](https://github.com/tinkoff-ai/etna/pull/921))
- `etna.auto` for greedy search, `etna.auto.pool` with default pipelines, `etna.auto.optuna` wrapper for optuna ([895](https://github.com/tinkoff-ai/etna/pull/895))
- Add `MinMaxDifferenceTransform` ([955](https://github.com/tinkoff-ai/etna/pull/955))
- Add wandb sweeps and optuna examples ([338](https://github.com/tinkoff-ai/etna/pull/338))
Changed
- Make slicing faster in `TSDataset._merge_exog`, `FilterFeaturesTransform`, `AddConstTransform`, `LambdaTransform`, `LagTransform`, `LogTransform`, `SklearnTransform`, `WindowStatisticsTransform`; make CICD test different pandas versions ([900](https://github.com/tinkoff-ai/etna/pull/900))
- Mark some tests as long ([929](https://github.com/tinkoff-ai/etna/pull/929))
- Fix to_dict with nn models and add unsafe conversion for callbacks ([949](https://github.com/tinkoff-ai/etna/pull/949))
Fixed
- Fix `to_dict` with function as parameter ([941](https://github.com/tinkoff-ai/etna/pull/941))
- Fix native networks to work with generated future equals to horizon ([936](https://github.com/tinkoff-ai/etna/pull/936))
- Fix `SARIMAXModel` to work with exogenous data on `pmdarima>=2.0` ([940](https://github.com/tinkoff-ai/etna/pull/940))
- Teach catboost to work with encoders ([957](https://github.com/tinkoff-ai/etna/pull/957))