[FEATURE] Add fit, predict parallel flow for multiple time-series data

[MichalChromcak]

Is your feature request related to a problem? Please describe.
Once the best models are selected via functional part (run_model_selection, resp. select_model_general) or object-oriented part (ModelSelector.select_model) one usually needs to re-fit the selected models (e.g. daily) and predict with them. This part is currently missing, while this core part might be called directly from hcrystalball.

Describe the solution you'd like

• Run CV with the persistence of ModelSelectorResults or minimal setup (only best_model might suffice here)

• Load persisted models

• Get the new training data

• Run the fit_predict flow, that takes data for a subset of the partitions for which one has the models

  • Split new data training data
  • Run fit mapped over the partitions
  • Run predict mapped over the partitions
  • Reduce predictions to 1 dataframe
  • Return predictions

Describe alternatives you've considered
Adopt loading of persisted models within the flow, but as this might be stored on disk, but also in some db, let's leave that out-of-scope.

Additional context
We should ensure consistency between CV and fit-predict (frequency should be the same, horizon might change I guess, ...)