Use case: I have sales of 90 products during the first 180 days since the product launch. I want to train an LSTM network to predict sales 4 weeks ahead given the last 7 days of sales. The model should be able to predict sales on brand new products (that are not a part of the 90) when they are released.
What is the best way to structure and split the data? Should I simply concatenate the datasets for all the 90 products into one? I have to somehow separate datasets for different products during training so the model doesn't train on the last data points from one dataset and the first data points from the next. I am using Tensorflow, keras in Python.