I have an assignment of modelling electricity consumtion based on time series defined in two different time frames, say one has a resolution of 5 seconds and the other one is 1 hour.
I want to use LSTM here. Since the training input for a LSTM network has a shape like
[samples, time steps, features], one cannot fit data from different time frames (it's possible, but putting them together on the features axis probably won't be efficient)
What's the standard approach for such problems? One idea coming to my mind is to train two LSTM networks separately, and then concatenate their output, feed it into dense layer and train it (while having the LSTM layers frozen).
Any hints and insights highly appreciated.