Hi guys, I'm testing some reconciliation algorithm...
# general
Hi guys, I'm testing some reconciliation algorithms from the
package and I want to test using a
model with some additional features. I couldn't find example of that, the one in the documentation is not using any additional features. Is this possible any way?
Thanks Kin, but I still can't see how I can use exogenous covariates in the reconciliation forecasting process. Is there some place where this is decsribed?
I don’t believe anyone has estimated reconciliation matrices using exogenous features yet. That is a very novel idea.
There might be some work where different reconciliation matrices are computed for different calendar events. But it is a guess. Usually one shares the matrix across the horizon.
If you have very different residual distributions based on seasonality, the idea makes a lot of sense.
Thanks Kin. Let me try to rephrase my question. I have a time series which I've aggregated with the aggregate function. It produces a time dataframe with a single unique_id which is <upper_level_id> when it's the upper level and it's <upper_level_id>/<lower_level_id. Now, I have some additional features like
, etc that I can use in the forecasting model. I'm curious if there's a quick way to integrate those in the model, i.e. do a left join or something.