jpeg729:

train_inp_time[sample :, :] = temp.values


I made this mistake while copying the code!



 jpeg729:

I consider every timestep to be a data sample for the RNN, because for each timestep the rnn takes an input, combines it with its memory of the previous inputs, and produces an output. How we organise the samples into short or long sequences is largely irrelevant from that point of view. That said, if seasonality or trends in the input history is at all useful in predicting the output, then longer sequences will be useful.


I see your point and it seems to be very valid! Of course, with a very long sequence, RNNs would suffer, and LSTMs, GRU might do much better.
That being said, I guess you’d be suggesting to use something like a single sequence per home of length num_days*24?



 jpeg729:

Day of week might be another useful feature to consider.


I see your point. I agree. If only I could get these additional features to work