Etna

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

Added
- MedianOutliersTransform, DensityOutliersTransform ([30](https://github.com/tinkoff-ai/etna/pull/30))
- Issues and Pull Request templates
- TSDataset checks ([24](https://github.com/tinkoff-ai/etna/pull/24), [#20](https://github.com/tinkoff-ai/etna/pull/20))\
- Pytorch-Forecasting models ([29](https://github.com/tinkoff-ai/etna/pull/29))
- SARIMAX model ([10](https://github.com/tinkoff-ai/etna/pull/10))
- Logging, including ConsoleLogger ([46](https://github.com/tinkoff-ai/etna/pull/46))
- Correlation heatmap plotter ([77](https://github.com/tinkoff-ai/etna/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/pull/55), [#53](https://github.com/tinkoff-ai/etna/pull/53), [#52](https://github.com/tinkoff-ai/etna/pull/52))
- Solved warning in LogTransform and AddConstantTransform ([26](https://github.com/tinkoff-ai/etna/pull/26))
- Regressors do not have enough history bug ([35](https://github.com/tinkoff-ai/etna/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/pull/62))
- Fix saving init params for SARIMAXModel ([81](https://github.com/tinkoff-ai/etna/pull/81))
- Imports of nn models, PytorchForecastingTransform and Transform ([80](https://github.com/tinkoff-ai/etna/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