Hi everybody, I am new here and I might be asking ...
# neural-forecast
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
when you instantiate a model, and it will only use other variables to produce the forecasts
you still need to pass the
variable to the training data, because it will use it for the training loss.