I am working with variable length time-series signals. I want to use a sliding window to extract features, things like mean, standard deviation, kurtosis, skewness. The length varies pretty significantly.
However for the training I will be using linear models like Logistic Regression and I need a constant input length.
I'm not sure how to equate the signals, should I use a fixed-size window and use the largest signal input length while padding the smaller signal features with zeros?
Should I use a variable window-size and divide the signals into a fixed predetermined number of segments?
I'd really appreciate some suggestions. Sorry if this is a duplicate, I could not find the answer, perhaps because I was searching for the wrong thing.