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Hi Merlin, Regarding your first question, I think that's a limitation inherent to the model and not just to StatsForecast's implementation. If you don't have the future values of the exogenous regressors, then you can forecast them first. This adds another layer of uncertainty, but might work with your data (you need to test it). I've done it before with prices when forecasting for a retailer and got acceptable results.

Okay, thank you for taking the time to respond 🙂 Future exogenous values are just taken from the training set for cross-validation. For pre-known future features like "is_weekend", 'is_holiday" this is a valid approach, but in case they are unknown, there is no separate prediction (cf. https://github.com/Nixtla/statsforecast/blob/main/statsforecast/core.py#L289). Maybe you could outline this to others in a tutorial. I suppose for more sophisticated predictions, I will be better off using neuralforcast then?

hey @Merlin! Currently, statsforecast doesn’t support the distinction between historical exogenous variables and future exogenous variables (available at the time of the prediction). For that kind of modeling, we suggest using neuralforecast :)

Is there a way to use exogenous variables with the AutoLSTM etc. in neuralforecast?

hey @Merlin! Yes, here’s an example of how to include exogenous variables: https://nixtla.github.io/neuralforecast/examples/exogenous_variables.html For the auto versions, you have to include the

futr_exog_list

,

hist_exog_list

, and

stat_exog_list

in your hyperparameter grid