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, ...)
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
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, ...)