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Hey @Nikola Shahpazov, I think these two links can help: https://nixtla.github.io/hierarchicalforecast/examples/mlframeworksexample.html https://nixtla.github.io/mlforecast/forecast.html#dynamic-features

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.

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

is_holiday

,

is_special_day

, 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.