I have some biological data (ECG), which are quite chaotic in nature, and and some other data; that are not chaotic but related in some way, like fatigue. I want to find out how the time series, chaotic, data are related to each of these other time series.
My undergrad thesis was on string "emotion" classification and time series is a whole other animal. My goal is to understand how the "other data" trend and at what percentage in correlation the biological data, not just classes [A, B, C].
I want to be able to distinct what is happening throughout the biological's phases in retrospect to the other time series. Like in this phase of the ECG there was 20% increase in fatigue, 7% in attention etc. I have all those data I just don't know how to make the connection.
I tried to find on time series ML but all of them jump from pretty basic algorithms and vague narratives to hardcore mathematical implementations.
If you know of a site, blog, docs, anything that ease into such examples and, very important, have code (python would be nice).
Would Random Forest work for such a task or should I be looking more into KNN (K-Means Nearest Neighbour), DTW (Dynamic Time Wrapping), STMF (Shape-based Template Matching Framework)?
Thank you very match for your time. Would be happy to provide and clarification I can, if anything doesn't make sense.