Statsforecast

What's Changed
* feat: add issues template by FedericoGarza in https://github.com/Nixtla/statsforecast/pull/93
* refactor: use Pool instead of ProcessPoolExecutor by FedericoGarza in https://github.com/Nixtla/statsforecast/pull/96
* Feat: add ray integration by FedericoGarza in https://github.com/Nixtla/statsforecast/pull/98
* fix: add automatic n_jobs behavior by FedericoGarza in https://github.com/Nixtla/statsforecast/pull/99
* Creation of forecast dates improvement by FedericoGarza in https://github.com/Nixtla/statsforecast/pull/101
* Ray experiment by FedericoGarza in https://github.com/Nixtla/statsforecast/pull/103
* Update README.md by mergenthaler in https://github.com/Nixtla/statsforecast/pull/104


**Full Changelog**: https://github.com/Nixtla/statsforecast/compare/v0.5.3...v0.5.4

What's Changed

New features

- `summary` method for the `AutoARIMA` class requested in 31.
- representational string for the `AutoARIMA` fitted model, requested in 83.

Bug Fixes

- [BUG] `croston_sba` 88 fixed in 89.

- Added `predict_in_sample` method for `AutoARIMA`.
- Users can now compute in sample forecasts including prediction intervals.

- Now: Good Ol' sklearn syntax with `model = AutoARIMA(); model.fit(y); model.predict(10)`.
- Bug fixes.

Notable changes

- Inclusion of `prediction intervals` for `auto_arima`.
- `statsforecast` is now installable from `conda-forge` (`conda install -c conda-forecast statsforecast`, thanks to sugatoray).

Notable changes

- Inclusion of `exogenous variables` for `auto_arima`.
- The `StatsForecast` class now handles `exogenous variables`.
- This release allows developers to include more models that use `exogenous variables`.
- Bug fixes.