I have a time series that I want to predict with an LSTM. I am able to get very good results using 50 datapoints predicting 51, but I struggle to get any accuracy using something like 200 datapoints to predict 220. After an epoch, my network outputs 0 for all inputs. Is there a technique for predicting multiple timesteps ahead of the final output with a neural network?
For example, would it make more sense to predict 1 timestep ahead 20 times in a row feeding the outputs back in to get to that 20th timestep? Training it on a sequence followed by the timestep 20 ahead does not seem to work so far.