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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?

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