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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?

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canbe tackled with a seq2seq model, since you have a sequence prediction problem. 2. It would suffer from an accumulation on prediction error (noise).I'm still studying about seq2seq so cannot comment on the 2 points above but I would recommend that you refer below tutorial from Dr Jason Brownlee and I am sure this is what you may be looking for- http://machinelearningmastery.com/multi-step-time-series-forecasting-long-short-term-memory-networks-python/

– Nitin Mahajan – 2017-08-09T12:30:13.803