I'm working on a problem with data from a continuous real-valued signal. The goal is to use ML to smooth the signal based off of past data. To accomplish this, the signal is windowed into a period that's meaningful within the domain. The problem is that this period is highly variable in length.
Seeing as denoising autoencoders are based off of matrix multiplication, this presents a serious problem. What is the standard approach in such a situation? Should I define an arbitrary (large) window size, and expand windows that are too small (and vice versa)? Or is there a better approach for variable length inputs?