How to make RNN time-forecast multiple days using Keras?
I don't have the rep to comment, but I'll say here that I've toyed with a similar task. One could use a sliding window approach for 90 days (I used 30, since 90 is pushing LSTM limits), then predict the price appreciation for next month (so your prediction is for a single value). @Digital-Thinking is generally right though, you shouldn't expect great performance.
I am currently working on a program that would take the previous 4000 days of stock data about a particular stock and predict the next 90 days of performance.
The way I've elected to do this is with an RNN that makes use of LSTM layers to use the previous 90 days to predict the next day's performance (when training, the previous 90 days are the x-values and the next day is used as the y-value). What I would like to do however, is use the previous 90-180 days to predict all the values for the next 90 days. However, I am unsure of how to implement this in Keras as all the examples I have seen only predict the next day and then they may loop that prediction into the next day's 90 day x-values.
Is there any ways to just use the previous 180 days to predict the next 90? Or is the LSTM restricted to only predicting the next day?