[ozankabak]

I don't understand if they use windowing as a fixed computational step that is active both in training and scoring time, or, if they use sliding windows only to chop up the training data.

Also, I wonder if they checked how a feed-forward NN that operates on the contents of a sliding window (e.g. as in the first approach above) compares with their RNN results. I am curious about this, as it would give us a hint whether the RNN's internal state encodes something that is not a simple transformation of the window contents. If this turns out to be the case, I'd then be interested in figuring out what the internal state "means"; i.e. whether there is anything there that we humans can recognize.

[edited to increase clarity]

[zebrafish]

I wasn't very sure what the sliding window part was about either. I think they were just saying that they trained on a sliding window using the "output window" as part of their loss function.

A feed-forward NN wouldn't do much because it doesn't hold a state variable which you need to be able to understand context in time series data. There are probably some pieces of the state that you'd be able to interpret but the majority of it would mean nothing to us.

[nlowell]

It makes very little sense to me that their sliding window does not appear to contain previous holidays. I'm not expert but I'm pretty sure holidays are a seasonal trend, and it would benefit them to train their models on previous holidays as opposed to the 3 months before a holiday, right?