I have been looking at recurrent neural networks and LSTM models for time series, and it is interesting that they can predict multiple days ahead. For example, I can take inputs of 100 days and predict 7 or 10 days into the future.
I was wondering if there is a way to do this type of multi-step time series prediction using a Bayesian time-series model? Now let me be a little more precise about what I mean. A bayesian model can obviously predict one day ahead, based upon the previous 1, 2 or 3 days. That is a simple bayesian auto-regressive process. But is it possible to simultaneously predict 2, 3, 4 or more days ahead, given training data for previous days or weeks or months?
Now, it is fine if the usual models and methods can only output a single day at a time, and hence cannot predict multiple days in advance. In those cases, I would predict one day in advance, append the prediction to the dataset and predict again iteratively. So that would work. If that is the case, then someone can simply indicate that. BUT I was not sure if this multiple outputs idea was possible with bayesian models?