Comment by lukas_b

This looks very interesting! I'm trying to understand if the flattening technique might work for my ts. It's structured as follows: At each time step t, I have an m by n data matrix. The value for m (rows) varies per time step. n stays constant and represents the features. And i want to predict one of the n values. (In this case, t represents a single day, m (rows) represent the people that entered a store on that day, and n (cols) represent various features of the people. I want to predict one of those features, given the others.) The fact that it's a time series matters, because i expect the relationship to change over time. For instance some feature n[x] (person wears a yellow shirt) might be correlated with my target feature n[y] (person steals) but only in the summer. would it be possible to flatten this too? What would that look like?