Hi everybody, I am new here and I might be asking a question which was already asked. I couldn't find any matches though, so I hope you'll forgive me. Here is the question: I am intending to develop a time series model in the fashion of our old econometric "distributed lags models". That means, that the forecast (even a nowcast) ist done only with past values of other variables. The own time series is not available for prediction. LSTMs and TCNs can handle it without difficulty but I would like to implement a TFT and a nbeats for I have the feeling that the structures might be interesting for the task. Any hint on how to move on will be much appreciated!

Hi @Luis Huergo! Yes, you can do it with all models. Set the hyperparameter

exclude_insample_y=True

when you instantiate a model, and it will only use other variables to produce the forecasts