Pmdarima

* Added `pmdarima.show_versions` as a utility for issue filing
* Fixed deprecation for `check_is_fitted` in newer versions of scikit-learn
* Adds the `pmdarima.datasets.load_sunspots()` method with R’s sunspots dataset
* Adds the `pmdarima.model_selection.train_test_split()` method
* Fix bug where 1.5.1 documentation was labeled version “0.0.0”.
* Fix bug reported in 271, where the use of threading.local to store stepwise context information may have broken job schedulers.
* Fix bug reported in 272, where the new default value of max_order can cause a ValueError even in default cases when stepwise=False.

Fixes a bug in v1.5.0 where the `pmdarima.__version__` attribute returned `0.0.0`

* No longer use statsmodels' `ARIMA` or `ARMA` class under the hood; only use
the `SARIMAX` model, which cuts back on a lot of errors/warnings we saw in the past.
(211)

* Defaults in the `ARIMA` class that have changed as a result of 211:

- `maxiter` is now 50 (was `None`)
- `method` is now 'lbfgs' (was `None`)
- `seasonal_order` is now `(0, 0, 0, 0)` (was `None`)
- `max_order` is now 5 (was 10) and is no longer used as a constraint when `stepwise=True`

* Correct bug where `aicc` always added 1 (for constant) to degrees of freedom,
even when `df_model` accounted for the constant term.

* New `pmdarima.arima.auto.StepwiseContext` feature for more control over
fit duration (introduced by [kpsunkara](https://github.com/kpsunkara) in #221.

* Adds the `pmdarima.preprocessing.LogEndogTransformer` class as discussed in
205

* Exogenous arrays are no longer cast to numpy array by default, and will pass pandas
frames through to the model. This keeps variable names intact in the summary 222

* Added the `prefix` param to exogenous featurizers to allow the addition of meaningful
names to engineered features.

* Added polyroot test of near non-invertibility when `stepwise=True`. For
models that are near non-invertible will be deprioritized in model selection
as requested in 208

* Removes `pmdarima.arima.ARIMA.add_new_samples`, which was previously deprecated.
Use `pmdarima.arima.ARIMA.update` instead.

* The following args have been deprecated from the `pmdarima.arima.ARIMA` class
as well as `pmdarima.arima.auto_arima` and any other calling methods/classes:

- `disp`^[1]
- `callback`^[1]
- `transparams`
- `solver`
- `typ`

[1] These can still be passed to the `fit` method via `**fit_kwargs`, but should
no longer be passed to the model constructor.

* Added `diff_inv` function that is in parity with [R's implementation](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/diffinv.html), as requested in #180

* Added `decompose` function that is in parity with [R's implementation](https://www.rdocumentation.org/packages/stats/versions/3.6.1/topics/decompose),
as requested in 190

* Fixes 191, an issue where the OCSB test could raise `ValueError: negative dimensions are not allowed" in OCSB test`

* Add option to automatically inverse-transform endogenous transformations when predicting
from pipelines (197)

* Add `predict_in_sample` to pipeline (196)

* Parameterize `dtype` option in datasets module

* Adds the `model_selection` submodule, which defines several different cross-validation
classes as well as CV functions:

- `pmdarima.model_selection.RollingForecastCV`
- `pmdarima.model_selection.SlidingWindowForecastCV`
- `pmdarima.model_selection.cross_validate`
- `pmdarima.model_selection.cross_val_score`

* Adds the `pmdarima.datasets.load_taylor` dataset

* Adds a new dataset for stock prediction, along with an associated example (`load_msft`)
* Fixes a bug in predict_in_sample, as addressed in 140.
* Numpy 1.16+ is now required
* Statsmodels 0.10.0+ is now required
* Added `sarimax_kwargs` to `ARIMA` constructor and `auto_arima` function. This fixes 146

This is a patch release specifically to get around the statsmodels issue:

https://github.com/statsmodels/statsmodels/issues/5747

This pins scipy at 1.12 until statsmodels releases 0.10.0 (at some point in June 2019). Additionally, deprecation warnings are fixed in the scikit-learn dependency.