Hi,
I’m creating a simple RNN which takes as input an hourly time series and outputs another hourly time series. We can think of the process as “denoising”.
I feed the data one day at a time, so basically, I feed sequences of 1-dim and length 24.
However, this vanilla RNN does not explicitly know that the first reading corresponds to 12 AM, next at 1 AM, and so on.
How can we incorporate this “time-of-day” feature into RNN?
One way could be to one-hot encode the hours, and then feeding in the concatenated 24 bit one-hot encoded sequence and the value at that time as an input for that time. This would lead to input being 25-dim and length  24, while output is sill 1-dim and length 24.
I feel this approach is odd, as it’s somehow forcing two different types of inputs (real-values v/s one-hot encoded) and also in a different range (say 0-1000 vs 0/1) to be a part of the same input sequence.
How would you suggest to incorporate time into RNN?