Clustering Multivariate Time Series Using Hidden Markov Models

Clustering Multivariate Time Series Using Hidden Markov Models

In this paper we describe an algorithm for clustering multivariate time series with variables taking both categorical and continuous values. Time series of this type are frequent in health care, where they represent the health trajectories of individuals. The problem is challenging because categorical variables make it difficult to define a meaningful distance between trajectories. We propose a...

Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series

Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods are compared with a recent hierarchical ...

Time Series Classiication Using Hidden Markov Models and Neural Networks

| This paper gives a brief overview on statistical classiication of time series using Hidden Markov Models. To overcome some limitations and assumptions made in the Hidden Markov Model framework two successful hybrid extensions using Neural Networks are presented. At rst, a short description of traditional stochastic-based Hidden Markov Model classiiers with diierent kinds of emission probabili...

Clustering Time Series with Hidden Markov Modelsand Dynamic Time

Clustering Time Series with Hidden Markov Models and Dynamic Time Warping

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 di erent 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 ...