How to deal with multi step time series forecasting in multivariate LSTM in keras

Question

I am trying to do multi-step time series forecasting using multivariate LSTM in Keras. Specifically, I have two variables (var1 and var2) for each time step originally. Havi

Answer 1

Actually you can't just feed in the raw time series data, as the network won't fit to it naturally. The current state of RNNs still requires you to input multiple 'features' (manually or automatically derived) for it to properly learn something useful.

Usually the prior steps needed are:

1. Detrend
2. Deseasonalize
3. Scale (normalize)

A great source of information is this post from a Microsoft researcher which won a time series forecasting competition by the means of a LSTM Network.

Also this post: CNTK - Time series Prediction