Etna-ts

Added
- BinsegTrendTransform, ChangePointsTrendTransform ([87](https://github.com/tinkoff-ai/etna-ts/pull/87))
- Interactive plot for anomalies ([95](https://github.com/tinkoff-ai/etna-ts/pull/95))
- Examples to TSDataset methods with doctest ([92](https://github.com/tinkoff-ai/etna-ts/pull/92))
- WandbLogger ([71](https://github.com/tinkoff-ai/etna-ts/pull/71))
- Pipeline ([78](https://github.com/tinkoff-ai/etna-ts/pull/78))
- Sequence anomalies ([96](https://github.com/tinkoff-ai/etna-ts/pull/96)), Histogram anomalies ([#79](https://github.com/tinkoff-ai/etna-ts/pull/79))
- 'is_weekend' feature in DateFlagsTransform ([101](https://github.com/tinkoff-ai/etna-ts/pull/101))
- Documentation example for models and note about inplace nature of forecast ([112](https://github.com/tinkoff-ai/etna-ts/pull/112))
- Property regressors to TSDataset ([82](https://github.com/tinkoff-ai/etna-ts/pull/82))
- Clustering ([110](https://github.com/tinkoff-ai/etna-ts/pull/110))
- Outliers notebook ([123](https://github.com/tinkoff-ai/etna-ts/pull/123)))
- Method inverse_transform in TimeSeriesImputerTransform ([135](https://github.com/tinkoff-ai/etna-ts/pull/135))
- VotingEnsemble ([150](https://github.com/tinkoff-ai/etna-ts/pull/150))
- Forecast command for cli ([133](https://github.com/tinkoff-ai/etna-ts/issues/133))
- MyPy checks in CI/CD and lint commands ([39](https://github.com/tinkoff-ai/etna-ts/issues/39))
- TrendTransform ([139](https://github.com/tinkoff-ai/etna-ts/pull/139))
- Running notebooks in ci ([134](https://github.com/tinkoff-ai/etna-ts/issues/134))
- Cluster plotter to EDA ([169](https://github.com/tinkoff-ai/etna-ts/pull/169))
- Pipeline.backtest method ([161](https://github.com/tinkoff-ai/etna-ts/pull/161), [#192](https://github.com/tinkoff-ai/etna-ts/pull/192))
- STLTransform class ([158](https://github.com/tinkoff-ai/etna-ts/pull/158))
- NN_examples notebook ([159](https://github.com/tinkoff-ai/etna-ts/pull/159))
- Example for ProphetModel ([178](https://github.com/tinkoff-ai/etna-ts/pull/178))
- Instruction notebook for custom model and transform creation ([180](https://github.com/tinkoff-ai/etna-ts/pull/180))
- Add inverse_transform in *OutliersTransform ([160](https://github.com/tinkoff-ai/etna-ts/pull/160))
- Examples for CatBoostModelMultiSegment and CatBoostModelPerSegment ([181](https://github.com/tinkoff-ai/etna-ts/pull/181))
- Simplify TSDataset.train_test_split method by allowing to pass not all values ([191](https://github.com/tinkoff-ai/etna-ts/pull/191))
- Confidence interval anomalies detection to EDA ([182](https://github.com/tinkoff-ai/etna-ts/pull/182))
- ConfidenceIntervalOutliersTransform ([196](https://github.com/tinkoff-ai/etna-ts/pull/196))
- Add 'in_column' parameter to get_anomalies methods([199](https://github.com/tinkoff-ai/etna-ts/pull/199))
- Clustering notebook ([152](https://github.com/tinkoff-ai/etna-ts/pull/152))
- StackingEnsemble ([195](https://github.com/tinkoff-ai/etna-ts/pull/195))
- Add AutoRegressivePipeline ([209](https://github.com/tinkoff-ai/etna-ts/pull/209))
- Ensembles notebook ([218](https://github.com/tinkoff-ai/etna-ts/pull/218))
- Function plot_backtest_interactive ([225](https://github.com/tinkoff-ai/etna-ts/pull/225))
- Confidence intervals in Pipeline ([221](https://github.com/tinkoff-ai/etna-ts/pull/221))

Changed
- Delete offset from WindowStatisticsTransform ([111](https://github.com/tinkoff-ai/etna-ts/pull/111))
- Add Pipeline example in Get started notebook ([115](https://github.com/tinkoff-ai/etna-ts/pull/115))
- Internal implementation of BinsegTrendTransform ([141](https://github.com/tinkoff-ai/etna-ts/pull/141))
- Colorebar scaling in Correlation heatmap plotter ([143](https://github.com/tinkoff-ai/etna-ts/pull/143))
- Add Correlation heatmap in EDA notebook ([144](https://github.com/tinkoff-ai/etna-ts/pull/144))
- Add `__repr__` for Pipeline ([151](https://github.com/tinkoff-ai/etna-ts/pull/151))
- Defined random state for every test cases ([155](https://github.com/tinkoff-ai/etna-ts/pull/155))
- Add confidence intervals to Prophet ([153](https://github.com/tinkoff-ai/etna-ts/pull/153))
- Add confidence intervals to SARIMA ([172](https://github.com/tinkoff-ai/etna-ts/pull/172))
- Add badges to all example notebooks ([220](https://github.com/tinkoff-ai/etna-ts/pull/220))
- Update backtest notebook by adding Pipeline.backtest ([222](https://github.com/tinkoff-ai/etna-ts/pull/222))

Fixed
- Set default value of `TSDataset.head` method ([170](https://github.com/tinkoff-ai/etna-ts/pull/170))
- Categorical and fillna issues with pandas >=1.2 ([190](https://github.com/tinkoff-ai/etna-ts/pull/190))
- Fix `TSDataset.to_dataset` method sorting bug ([183](https://github.com/tinkoff-ai/etna-ts/pull/183))
- Undefined behaviour of DataFrame.loc[:, pd.IndexSlice[:, ["a", "b"]]] between 1.1.* and >= 1.2 ([188](https://github.com/tinkoff-ai/etna-ts/pull/188))
- Fix typo in word "length" in `get_segment_sequence_anomalies`,`get_sequence_anomalies`,`SAXOutliersTransform` arguments ([212](https://github.com/tinkoff-ai/etna-ts/pull/212))
- Make possible to send backtest plots with many segments ([225](https://github.com/tinkoff-ai/etna-ts/pull/225))

Fixed
- Limit version of pandas by 1.2 (excluding) ([163](https://github.com/tinkoff-ai/etna-ts/pull/163))

Changed
- SklearnTransform out column names ([99](https://github.com/tinkoff-ai/etna-ts/pull/99))
- Update EDA notebook ([96](https://github.com/tinkoff-ai/etna-ts/pull/96))
- Add 'regressor_' prefix to output columns of LagTransform, DateFlagsTransform, SpecialDaysTransform, SegmentEncoderTransform
Fixed
- Add more obvious Exception Error for forecasting with unfitted model ([102](https://github.com/tinkoff-ai/etna-ts/pull/102))
- Fix bug with hardcoded frequency in PytorchForecastingTransform ([107](https://github.com/tinkoff-ai/etna-ts/pull/107))
- Bug with inverse_transform method of TimeSeriesImputerTransform ([148](https://github.com/tinkoff-ai/etna-ts/pull/148))

Fixed
- Documentation build workflow ([85](https://github.com/tinkoff-ai/etna-ts/pull/85))

Added
- MedianOutliersTransform, DensityOutliersTransform ([30](https://github.com/tinkoff-ai/etna-ts/pull/30))
- Issues and Pull Request templates
- TSDataset checks ([24](https://github.com/tinkoff-ai/etna-ts/pull/24), [#20](https://github.com/tinkoff-ai/etna-ts/pull/20))\
- Pytorch-Forecasting models ([29](https://github.com/tinkoff-ai/etna-ts/pull/29))
- SARIMAX model ([10](https://github.com/tinkoff-ai/etna-ts/pull/10))
- Logging, including ConsoleLogger ([46](https://github.com/tinkoff-ai/etna-ts/pull/46))
- Correlation heatmap plotter ([77](https://github.com/tinkoff-ai/etna-ts/pull/77))

Changed
- Backtest is fully parallel
- New default hyperparameters for CatBoost
- Add 'regressor_' prefix to output columns of LagTransform, DateFlagsTransform, SpecialDaysTransform, SegmentEncoderTransform

Fixed
- Documentation fixes ([55](https://github.com/tinkoff-ai/etna-ts/pull/55), [#53](https://github.com/tinkoff-ai/etna-ts/pull/53), [#52](https://github.com/tinkoff-ai/etna-ts/pull/52))
- Solved warning in LogTransform and AddConstantTransform ([26](https://github.com/tinkoff-ai/etna-ts/pull/26))
- Regressors do not have enough history bug ([35](https://github.com/tinkoff-ai/etna-ts/pull/35))
- make_future(1) and make_future(2) bug
- Fix working with 'cap' and 'floor' features in Prophet model ([62](https://github.com/tinkoff-ai/etna-ts/pull/62))
- Fix saving init params for SARIMAXModel ([81](https://github.com/tinkoff-ai/etna-ts/pull/81))
- Imports of nn models, PytorchForecastingTransform and Transform ([80](https://github.com/tinkoff-ai/etna-ts/pull/80))

Added
- Models
- CatBoost
- Prophet
- Seasonal Moving Average
- Naive
- Linear
- Transforms
- Rolling statistics
- Trend removal
- Segment encoder
- Datetime flags
- Sklearn's scalers (MinMax, Robust, MinMaxAbs, Standard, MaxAbs)
- BoxCox, YeoJohnson, LogTransform
- Lag operator
- NaN imputer
- TimeSeriesCrossValidation
- Time Series Dataset (TSDataset)
- Playground datasets generation (AR, constant, periodic, from pattern)
- Metrics (MAE, MAPE, SMAPE, MedAE, MSE, MSLE, R^2)
- EDA methods
- Outliers detection
- PACF plot
- Cross correlation plot
- Distribution plot
- Anomalies (Outliers) plot
- Backtest (CrossValidation) plot
- Forecast plot