Enhance integration of Global and Local models.

[davide-burba]

Is your feature request related to a current problem? Please describe.

When using a mixture of local and global models, the user needs to distinguish the model types.

Here's a list of practical examples:

• When calling the fit method, local models don't support lists of TimeSeries.
• Ensembles support a mixture of local and global models when calling the historical_forecasts method, but not when calling the fit method.
• It's not clear if global models are effectively trained on multiple time-series when using the historical_forecasts method, especially when using ensembles.

Describe proposed solution

• Add support for multiple time-series on local models. Under the hood, independent models should be trained.
• Allow to fit and predict ensembles of mixtures of local/global models.
• Provide a single interface wrapper to call fit, predict, and historical_forecasts on any kind of model. Under the hood, the interface should assign the correct args to fit each model, and raise an error if some args are missing, and possibly raise a warning if some args are unused.
• Bonus: it would be cool to have a factory that receives the name of the model and a serializable dict of args to create instances of models, or even ensembles.