I'm using the keras package in order to train an LSTM for a univariate time series of type numeric (float). Performing a 1-step ahead forecast is trivial, but I'm not sure how to perform a, let's say, 10-step ahead forecast. Two questions:
1) I read about sequence to sequence NNs, but can barely find anything of it in the context of time series forecasting. Am I right with the assumption that the forecasting of more than 1 time step in advance is a seq2seq problem? That makes sense to me because each forecast depends on its predecessor.
2) An intuitive solution without seq2seq would be: Perform 1-step ahead forecast, then append this forecast to the series and use it to obtain the next forecast, and so on. How would this differ from a seq2seq approach?