Hello Team, I have a question regarding to the model training associated with the 'id_col' in the data. And the 'id_col' exits in NeuralForecast, MLForecast, and TimeGPT As we know that 'id_col' is symbol for multi-series. And for the model that naturally can have multi-series as input (partly in NeuralForecast), the trained model will be the global model (one model for all). However, for the model that does not naturally have multi-series as input (most in MLForecast), I wonder it will have three possible scenarios. 1. Automatic add 'id_col' to 'static_features', with the ordinal encoding (categorial value to integer), so the 'id_col' becomes an extra features to training the global model. 2. Training individual models for each series ('id') in the multi-series, and so the resultant trained models would be costly if the multi-series has many unique series. 3. Training the global model for all the series, ignore the 'id_col' (drop it), and so the resultant trained model would output same value for every series (the series has no intrinsic difference). Please correctify me and explain what's the actual strategy when a model (which naturally does not handle multi-series) dealing with multi-series that we use for training. Many Thanks!

Hey. Are you asking how does it work by default?

Yes, please!

The

id_col

isn't added as a feature unless you do explicitly, e.g.

static_features=[id_col]

. Also a global model is always trained, the only library that trains one model per id is statsforecast