# Clustering Time Series and the Surprising Robustness of HMMs

@article{Kozdoba2016ClusteringTS, title={Clustering Time Series and the Surprising Robustness of HMMs}, author={Mark Kozdoba and Shie Mannor}, journal={ArXiv}, year={2016}, volume={abs/1605.02531} }

Suppose that we are given a time series where consecutive samples are believed to come from a probabilistic source, that the source changes from time to time and that the total number of sources is fixed. Our objective is to estimate the distributions of the sources. A standard approach to this problem is to model the data as a hidden Markov model (HMM). However, since the data often lacks the Markov or the stationarity properties of an HMM, one can ask whether this approach is still suitable… CONTINUE READING

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