Pmdarima

* Pins `statsmodels` <0.12 to get around single-step forecasts with an exog array
* Fixes new issues introduced by latest `setuptools`
* Deprecate Python 3.5 support, which will be removed in the next release cycle

-----

* Address issue 341 where a combination of a large `m` value and `D` value could difference an array into
being too small to test stationarity in the ADF test

* Fix issue 351 where a large value of `m` could prevent the seasonality test from completing.

* Fix issue 354 where models with near non-invertible roots could still be considered as candidate best-fits.

* Remove legacy pickling behavior that separates the statsmodels object from the pmdarima
object. This breaks backwards compatibility with versions pre-1.2.0.

* Change default `with_intercept` in `pmdarima.arima.auto_arima` to `'auto'` rather than
`True`. This will behave much like the current behavior, where a truthiness check will still return
True, but allows the stepwise search to selectively change it to `False` in the presence of certain
differencing conditions.

* Inverse endog transformation is now supported when `return_conf_int=True` on pipeline predictions

* Fix a bug where the `pmdarima.model_selection.SlidingWindowForecastCV` could produce
too few splits for the given input data.

* Permit custom scoring metrics to be passed for out-of-sample scoring, as requested in 368

* Pin Cython to be `>=0.29,<0.29.18`
* Pin statsmodels to be `>=0.11`

- Support newest versions of matplotlib
- Add new level of `auto_arima` error actions: "trace" which will warn for errors while dumping the original stacktrace.
- New featurizer: [`pmdarima.preprocessing.DateFeaturizer`](https://github.com/alkaline-ml/pmdarima/blob/e2bfd5d03547d20979c6afda3b832825ba0217df/pmdarima/preprocessing/exog/dates.py#L23). This can be used to create dummy and ordinal exogenous features and is useful when modeling pseudo-seasonal trends or time series with holes in them.
- Removes first-party conda distributions (see 326)
- Raise a `ValueError` in `arima.predict_in_sample` when `start < d`

Release candidate for 1.6.0 release

* Adds first-party conda distributions as requested in 173
- Due to dependency limitations, we only support 64-bit architectures and Python 3.6 or 3.7
* Adds Python 3.8 support as requested in 199
* Added `pmdarima.datasets.load_gasoline`
* Added integer levels of verbosity in the `trace` argument
* Added support for statsmodels 0.11+
* Added `pmdarima.model_selection.cross_val_predict`, as requested in 291