Etna

Added
- Add `BinaryOperationTransform` to transforms ([260](https://github.com/etna-team/etna/pull/260))
- Add `TFTNativeModel` ([290](https://github.com/etna-team/etna/pull/290))
- Add warning on trying to pass numeric timestamp if freq is not None and add `_cast_index_to_datetime` ([214](https://github.com/etna-team/etna/pull/214))
- Add `infer_alignment`, `apply_alignment`, `make_timestamp_df` into `etna.dataset.utils` ([256](https://github.com/etna-team/etna/pull/256))
- Add `TSDataset.create_from_misaligned` constructor ([269](https://github.com/etna-team/etna/pull/269))
- Add tutorial about working with misaligned data ([288](https://github.com/etna-team/etna/pull/288))
- Add in `OutliersTransform` possibilities use `ignore_flag_column` to skip values use ignore ([291](https://github.com/etna-team/etna/pull/291))

Changed
- Update glossary with terms related to working with misaligned data ([288](https://github.com/etna-team/etna/pull/288))
- Add ignoring of integer timestamp as a feature into native DL models ([210](https://github.com/etna-team/etna/pull/210))
- Update `pytorch_forecasting` models to handle integer timestamp ([208](https://github.com/etna-team/etna/pull/208))
- Update `datasets` module to work with integer timestamp ([146](https://github.com/etna-team/etna/pull/146))
- Add tests for `transform` on data with integer timestamp ([153](https://github.com/etna-team/etna/pull/153))
- Add tests for `models` on data with integer timestamp ([188](https://github.com/etna-team/etna/pull/188))
- Update `DateFlagsTransform`, `TimeFlagsTransform`, `HolidayTransform`, `SpecialDaysTransform`, `FourierTransform` to work with external timestamp ([169](https://github.com/etna-team/etna/pull/169))
- Update `analysis` module to work with integer timestamp ([161](https://github.com/etna-team/etna/pull/161))
- Update `StatsForecastARIMAModel`, `StatsForecastAutoARIMAModel`, `StatsForecastAutoCESModel`, `StatsForecastAutoETSModel`, `StatsForecastAutoThetaModel` to handle integer timestamp ([197](https://github.com/etna-team/etna/pull/197))
- Update `MRMRFeatureSelectionTransform` to handle integer timestamp ([164](https://github.com/etna-team/etna/pull/164))
- Update deseasonality transforms (`STLTransform`, `DeseasonalityTransform`) to handle integer timestamp ([174](https://github.com/etna-team/etna/pull/174))
- Update `HoltModel`, `HoltWintersModel`, `SimpleExpSmoothingModel`, `SARIMAXModel`, `AutoARIMAModel` to handle integer timestamp ((200)[https://github.com/etna-team/etna/pull/200])
- Update detrend transforms (`LinearTrendTransform`, `TheilSenTrendTransform`) to handle integer timestamp ([163](https://github.com/etna-team/etna/pull/163))
- Update `ResampleWithDistributionTransform` to work with integer timestamp ([165](https://github.com/etna-team/etna/pull/165))
- Update change point transforms (`ChangePointsSegmentationTransform`, `ChangePointsTrendTransform`, `ChangePointsLevelTransform`, `TrendTransform`) to handle integer timestamp ([176](https://github.com/etna-team/etna/pull/176))
- Update `BATSModel`, `TBATSModel` models to work with integer timestamp ([195](https://github.com/etna-team/etna/pull/195))
- Update `ProphetModel` to handle external timestamp ([203](https://github.com/etna-team/etna/pull/203))
- Remove checking frequency in `timestamp_column` of `ProphetModel` ([222](https://github.com/etna-team/etna/pull/222))
- Update `FourierTransform` to handle external datetime timestamp ([223](https://github.com/etna-team/etna/pull/223))
- Update `FoldMask` to work with integer timestamp, in `validate_on_dataset` method add validation on presence of `FoldMask` parameters in `ts.index`, add tests for `FoldMask` ([226](https://github.com/etna-team/etna/pull/226))
- Fix `FourierTransform` on integer index, add inference tests ([230](https://github.com/etna-team/etna/pull/230))
- Update outliers transforms to handle integer timestamp ([229](https://github.com/etna-team/etna/pull/229))
- Update pipelines to handle integer timestamp ([241](https://github.com/etna-team/etna/pull/241))
- Add `timestamp_range` and refactor code with it ([244](https://github.com/etna-team/etna/pull/244))
- Update CLI to handle integer timestamp ([246](https://github.com/etna-team/etna/pull/246))
- Update `ExogShiftTransform` to handle integer timestamp ([254](https://github.com/etna-team/etna/pull/254))
- Extend base `TSDataset` constructor to handle long format dataframes, update documentation and tutorials with this change ([266](https://github.com/etna-team/etna/pull/266))
- Update internal datasets to work with unaligned data ([292](https://github.com/etna-team/etna/pull/292))
- Speed up "timestamp" transforms ([295](https://github.com/etna-team/etna/pull/295)

Fixed
- Fix `PredictionIntervalOutliersTransform` fails to work with created columns ([291](https://github.com/etna-team/etna/pull/291))
- Prohibit empty list value and duplication of `target_timestamps` parameter in `FoldMask` ([226](https://github.com/etna-team/etna/pull/226))
- Fix `DeseasonalityTransform` fails to inverse transform short series ([174](https://github.com/etna-team/etna/pull/174))
- Fix indexing in `stl_plot`, `plot_periodogram`, `plot_holidays`, `plot_backtest`, `plot_backtest_interactive`, `ResampleWithDistributionTransform` ([244](https://github.com/etna-team/etna/pull/244))
- Fix `DifferencingTransform` to handle integer timestamp on test ([244](https://github.com/etna-team/etna/pull/244))
- Fix `HolidayTransform` to handle integer timestamp in `days_count` mode ([285](https://github.com/etna-team/etna/pull/285))

Added
- Add `electricity` to internal datasets ([60](https://github.com/etna-team/etna/pull/60))
- Add `parts` argument to `load_dataset` function ([79](https://github.com/etna-team/etna/pull/79))
- Add `M4` to internal datasets ([83](https://github.com/etna-team/etna/pull/83))
- Add `M3` to internal datasets ([91](https://github.com/etna-team/etna/pull/91))
- Add `traffic_2008` to internal datasets ([94](https://github.com/etna-team/etna/pull/94))
- Add `traffic_2015` to internal datasets ([100](https://github.com/etna-team/etna/pull/100))
- Add `tourism` to internal datasets ([120](https://github.com/etna-team/etna/pull/120))
- Add `weather` to internal datasets ([125](https://github.com/etna-team/etna/pull/125))
- Add `ETT` to internal datasets ([134](https://github.com/etna-team/etna/pull/134))
- Add `list_datasets` function ([145](https://github.com/etna-team/etna/pull/149))
- Add `IHEPC` to internal datasets ([150](https://github.com/etna-team/etna/pull/150))
- Add dataset integrity check using hash for internal datasets ([151](https://github.com/etna-team/etna/pull/151))
- Create page about internal datasets in documentation ([175](https://github.com/etna-team/etna/pull/175))
- Add usage example of internal datasets in `101-get_started.ipynb` and `305-classification.ipynb` tutorials ([202](https://github.com/etna-team/etna/pull/202))
- Add new `mode="days_count"` to `HolidayTransform`([239](https://github.com/etna-team/etna/issues/239))
- Add size method to `TSDataset` class ([238](https://github.com/etna-team/etna/pull/238))
- Add the `index_only` parameter to outlier analysis functions for return type control ([231](https://github.com/etna-team/etna/pull/231))

Changed
- Add `relevance_aggregation_mode` and `redundancy_aggregation_mode` into `MRMRFeatureSelectionTransform.params_to_tune` ([212](https://github.com/etna-team/etna/pull/212))
- Optimized `DensityOutliersTransform` and removed `_save_original_values` from outlier transforms ([231](https://github.com/etna-team/etna/pull/231))
- Update python to 3.10 in CI ([251](https://github.com/etna-team/etna/pull/251))

Fixed
- Fix `traffic_2008` ([128](https://github.com/etna-team/etna/pull/128))
- Fix number of segments in docs, column name for tourism dataset and change default save path ([206](https://github.com/etna-team/etna/pull/206))
- Fix method `to_dict` for `SklearnPerSegmentModel` and `SklearnMultiSegmentModel` ([199](https://github.com/etna-team/etna/pull/199))
- Fix method `fit` for `MRMRFeatureSelectionTransform` with `redundancy_aggregation_mode`=`median` ([212](https://github.com/etna-team/etna/pull/212))
- Fix method `predict_components` for `_CatBoostAdapter` working incorrectly on shuffled columns ([227](https://github.com/etna-team/etna/pull/227))

Added
- Add `params_to_tune` for `DeepStateModel` ([115](https://github.com/etna-team/etna/issues/115))
- Handle new functionality for prediction intervals in the `plot_forecast` ([130](https://github.com/etna-team/etna/pull/130))
- Add `get_historical_forecasts` to pipelines for forecast estimation at each fold on the historical dataset ([143](https://github.com/etna-team/etna/pull/143))
- `ConformalPredictionIntervals` method for prediction intervals estimation ([152](https://github.com/etna-team/etna/pull/152))
- Add `DeepARNativeModel` ([114](https://github.com/etna-team/etna/pull/114))
- `EmpiricalPredictionIntervals` method for prediction intervals estimation ([173](https://github.com/etna-team/etna/pull/173))
- Prediction intervals tutorial notebook ([189](https://github.com/etna-team/etna/pull/189))

Changed
- Change warning condition on loading object saved under different library version ([31](https://github.com/etna-team/etna/issues/31))

Fixed
- Speed up segment column creation in `TSDataset.to_hierarchical_dataset` ([194](https://github.com/etna-team/etna/pull/194))
- Speed up `BasePipeline._validate_backtest_dataset` ([194](https://github.com/etna-team/etna/pull/194))
- Speed up `datasets.utils.duplicate_data` ([194](https://github.com/etna-team/etna/pull/194))

Added
- Handle prediction intervals similar to target components in `TSDataset` ([97](https://github.com/etna-team/etna/pull/97))
- `SavePredictionIntervalsMixin` for the `BasePredictionIntervals` ([87](https://github.com/etna-team/etna/pull/87))
- Base class `BasePredictionIntervals` for prediction intervals into experimental module ([86](https://github.com/etna-team/etna/pull/86))
- Add `fit_params` parameter to `etna.models.sarimax.SARIMAXModel` ([69](https://github.com/etna-team/etna/pull/69))
- Add `quickstart` notebook, add `mechanics_of_forecasting` notebook ([1343](https://github.com/tinkoff-ai/etna/pull/1343))
- Add gallery of tutorials divided by level ([46](https://github.com/etna-team/etna/pull/46))
- Create documentation page with links to external resources ([44](https://github.com/etna-team/etna/pull/44))
- Add documentation page with glossary of terms ([45](https://github.com/etna-team/etna/pull/45/))
- Add publishing into s3 for the latest documentation version ([50](https://github.com/etna-team/etna/pull/50))
- Add publishing into s3 during release ([53](https://github.com/etna-team/etna/pull/53))
- Add multiversion switcher into documentation ([55](https://github.com/etna-team/etna/pull/55))
- Add error page into documentation ([57](https://github.com/etna-team/etna/pull/57))
- Add `LimitTransform` ([63](https://github.com/etna-team/etna/pull/63))
- Add config for Codecov to control CI ([80](https://github.com/etna-team/etna/pull/80))
- Add `EventTransform` ([78](https://github.com/etna-team/etna/pull/78))
- `NaiveVariancePredictionIntervals` method for prediction quantiles estimation ([109](https://github.com/etna-team/etna/pull/109))
- Update interval metrics to work with arbitrary interval bounds ([113](https://github.com/etna-team/etna/pull/113))

Changed
- Refactored transform inversion logic in `Pipeline` `forecast` method ([72](https://github.com/etna-team/etna/pull/72))
- Add parameter `save_ts` to pipeline method `fit` ([73](https://github.com/etna-team/etna/pull/73))
- Add installation page and notes about extensions into documentation of public classes ([1339](https://github.com/tinkoff-ai/etna/pull/1339))
- Merge User Guide and API sections in documentation, limit classes to show in API section ([1324](https://github.com/tinkoff-ai/etna/pull/1324))
- Unify example notebooks, rerun example notebooks ([1330](https://github.com/tinkoff-ai/etna/pull/1330))
- Rework `get_started` notebook ([1343](https://github.com/tinkoff-ai/etna/pull/1343))
- Add missing classes from decomposition into API Reference, add modules into page titles in API Reference ([61](https://github.com/etna-team/etna/pull/61))
- Update `CONTRIBUTING.md` with scenarios of documentation updates and release instruction ([77](https://github.com/etna-team/etna/pull/77))
- Set up sharding for running tests ([99](https://github.com/etna-team/etna/pull/99))
- Rework saving DL models by separating saving model's hyperparameters and model's weights ([98](https://github.com/etna-team/etna/pull/98))
- Deprecated `FutureMixin` ([58](https://github.com/etna-team/etna/pull/58))

Fixed
- Fix `ResampleWithDistributionTransform` working with categorical columns ([82](https://github.com/etna-team/etna/pull/82))
- `TSDataset._hierarchical_structure_from_level_columns` to support `pandas>=1.4,<1.5`([107](https://github.com/etna-team/etna/pull/107))
- Fix links from tinkoff-ai/etna to etna-team/etna ([47](https://github.com/etna-team/etna/pull/47))
- Fix CI job `cron-delete-untagged-images` ([95](https://github.com/etna-team/etna/pull/95))
- Rendering table of contents in notebooks ([1343](https://github.com/tinkoff-ai/etna/pull/1343))
- Fix formatting of docstrings, fix links from netlify to docs.etna.ai ([62](https://github.com/etna-team/etna/pull/62))
- Fix multiple warnings, revert catching warnings during testing ([105](https://github.com/etna-team/etna/pull/105))
- Fix bug with `numpy.warnings` in `numpy>=1.24`, rework building docker images to use `poetry.lock` ([116](https://github.com/etna-team/etna/pull/116))
- Fix name of steps in `publish` CI ([119](https://github.com/etna-team/etna/pull/119))

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))