Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series

Abstract

Given a source of time series data, such as the stock market or the monitors in an intensive care unit, there is often utility in determining whether there are qualitatively different regimes in the data and in characterizing those regimes. For example, one might like to know whether the various indicators of a patient’s health measured over time are being produced by a patient who is likely to live or one that is likely to die. In this case, there is a priori knowledge of the number of regimes that exist in the data (two), and the regime to which any given time series belongs can be determined post hoc (by simply noting whether the patient lived or died). However, these two pieces of information are not always present.