I am setting up a neural network that will predict the incoming customers at a store for the next seven days (the output is a list with seven numbers, one for each day). As input, I will give the neural network the last x weeks of data. The data contains the number of visitors for each day and information about that date, like what day it was (Monday, Tuesday, etc.). The optimal x has yet to be determined.
I split the data in a training- (80%) and testset (20%).
For training the neural network I am now using walk forward optimization where I take the first x weeks of the trainingset as input and use the next seven days after that as output. This is one input-output pair. For the next pair, I move the window one day. The input loses the first day, but gains the first day of the output of the previous pair. The output gains a whole new day. I hope the picture clarifies what I am talking about.
I wonder if this method will cause data-leakage, since I am giving the neural network output that it has already seen, one input-output pair before. I guess this would be solved by shifting the pairs by seven days instead of one, but this leaves me with very little data to train on. Keep in mind that I use this method for training the network. When testing the method, I am using data that the neural network has never seen before.