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?
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