clarification on the sliding length and padding

[m13ammed]

Thank you for your great contribution. I was unable to understand the difference in usage for the sliding length and sliding padding. For example, if I wanted to utilize X days for a forecasting problem, what would be the proper usage for the parameters be?

Thank you in advance.

sliding_length
sliding_padding

Note: I noticed on my dataset that using 24 =>sliding length > 1 yields better results, however for sliding length >24 a size mismatch error occurs at evaluation.
The impact for increasing the padding was less impactful than the length, so if you can clarify the proper usage it would be great.

[zhihanyue]

This figure illustrates the process of causal sliding inference. "sliding_length" is the step size of the sliding window. For each window, we require the representations on a slice with size "sliding_length". The "padding" only provides the contextual information for that slice, and we ignore the representation of the "padding". Commonly, sliding_length should be set to 1, and sliding_padding is the window size (i.e. X) for historical data.

[m13ammed]

This clears things up. Thank you