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The missing data is nonexistent data or truly missing because it was not collected or other issues with data collection/processing? If it is nonexistent data (e.g., an item was not sold on Monday) then you can use intermittent forecasting techniques. Nixtla has bunch of them (croston, adida, imapa). If the data is truly missing then you need to make a decision on how to impute it.

Is there any way to implement machine learning algorithm like light gbm in statsforecast? for multiple time series?