I'm dealing with a task where I need to forecast the n-ith value of a target variable in a multivariate time series. But in this case we have two variables: -var1: Is my target variable that represents the output of a system. -var2: This time series represents a binary control signal (on/off).
Thus, var1 varies according to its previous values and according to the control signal (var2).
My problem is that the output in a given day n depends on the last n values of var 2 and the last n-1 values of var 1. That is, I have a different number of values (n and n-1) as the input of my network.
In this scenario, I'm not sure about how to model the input of my network.
I was trying to use a LSTM layer as the input of my network, with 2 dimensions (var1 and var2). But, as I said, each "sample" has n values of var2 and n-1 values of var1. It is not possible to create a 2D array in this situation.