I am currently working on a forecasting project, where I use the GroupedForecastingProcessor to generate forecasts. This Processor has multiple models which each have a number of parameters such as:
- tree depth
- features used for training
- lag-parameters for ARIMAX
Is there a way to access these parameters and save them with the output of the forecasting so that I can compare the performance of multiple model runs in light of the parameters I used?
I know that one can at least use variables for some of the parameters. But I don’t know how to apply this approach to features, as these are chosen through checkboxes in the Processor. Besides, I don’t really like the variable approach.
Thanks in advance!