Hi there, I see the use of exogeneous variables for predictions require knowing their future values, but is there a way to use their lags for predictions when we know they have significant impact in the predictions?

Hey. You should be able to do that if you want to, you probably just need to change the dates

NF supports both historical and lagged features but I’m not sure if a single column can be in both

@José Morales Thanks. But changing the dates is not clear to me. Could you elaborate? Indeed in the data, target and exogeneous variables are in the same range of dates. Moreover I have mathematical guarantees that lagged exogeneous variables impact forecasting. My concern is how to use that in the fitting and then in the forecasting. Thanks

Which library are you using?

Statsforecast and MLforecast. both require having future exogeneous variables

In statsforecast I think the dates aren't checked, so you can just provide a dataframe with the correct shape, e.g. if you have 10 series and are forecasting 5 periods it should have 50 rows. In mlforecast the dates are checked, so you'd have to offset the dates in your future dataframe so that the first date is the one immediately after your last training date for each serie.

Hi @José Morales, quick question. Do Statsforecast and MLforecast handle non-stationary data? if yes how please?

Hey. In statsforecast it depends on the model, the ARIMA model for example will take differences and seasonal differences to try to make it stationary. In mlforecast you can use target transformations

Great Thanks

Hey. To restore the original values we only invert the transformation, e.g. for the standard scaler we multiply by the standard deviation and add back the mean

Ok Thanks. I'm asking because I know some simple inverse (like power transform, box_cox not sure for difference) induce bias, that needs to be corrected, in the "original" space. https://robjhyndman.com/hyndsight/backtransforming/ https://github.com/scikit-learn/scikit-learn/issues/15881

They don't adjust for bias at the moment