I would like to apply methods like Gramian angular field, recurrence plots and Markov transition fields to a time series classification (TSC) problem where the time series themselves are all of unequal lengths. I wondered if there exist some elegant ways to perform this transform without doing something 'blunt' like zero-padding the time-series.
I did come across the idea of rescaling/resizing post-transformation in https://ai.stackexchange.com/questions/6274/how-can-i-deal-with-images-of-variable-dimensions-when-doing-image-segmentation. They also mention the so-called SPP-net which does look cool : https://arxiv.org/pdf/1406.4729.pdf. However it does require that I want to solve the problem using a deep model (which I might not want to do in the end), and I do see some susceptibility to over-fitting on first glance (especially, if one has a relatively small dataset - which I do). However, this would still require the time series lengths are all the same as input (although I suppose one could consider batching the time series into near-about the same length and padding/downsampling accordingly).
Some preceding points:
One may argue that the easiest thing to do is to look into various summary statistics to generate features and do the TSC from there. I have done this, I do get some results - but I am concerned the these stats are missing something that a visual representation of the problem won't miss.
One may argue that I should just zero-pad (subject to context which in my case; is fine), and go from there. This is probably ok for sequential architectures, but this might corrupt (?) the visual representation. I guess I'm asking if there is a way out of having to do this?
One may insist that I explain the contextual reason for having unequal length time series, this is something I cannot really get into and I strongly do not believe it helps with the technical problem of dealing with image representations of unequal time series. I appreciate that I am being difficult here - if it is impossible to help - I can close the question.