I have been looking for a way to classify events in a time-series I have. My data is streaming to the cloud so computation time is not really an issue. The data is coming in every second for up to 4 - 5 hours. During that time there are events that cause spikes in the data. I did some basic analysis using spectral power to "see" these events, which mostly works, but I need to distinguish types of events. I attempted kNN where I computed three different types of spectral entropy when an event is seen. I could not see any clustering using kNN.
I found the package
tsfresh and questions like this: Time-series classification - very poor results but when I mimic their results my output for
features_filtered = select_features(extracted_features, y)
is empty. However, the output from
extracted_features = extract_features(df, column_id="id", column_sort="t") yields 618 features.
My first question is do I need to shorten the data to around where the event occurs? Right now I am simply marking each dataset as a binary of event or non-event. Secondly, it seems strange I would get 0
select_features. Are there any sanity checks to help me out?