I have a time-series dataset that is poisson-distributed and each day I get a new datapoint. If I input all the data into a HMM (hmmlearn in python) it does a very good job at estimating the hidden (binary) states over the historic data. Unfortunately it does a very poor job at classifying states (and its changes) in the most recent data-points, which is actually the data that I am most interested in classifying.

If I want to do a (binary) state classification, where the most recent data-point(s) is also the most important, are there any methods that are better suited for this than HMMs? I.e. are there any algorithms for classifying states in live data?



Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Browse other questions tagged or ask your own question.