Analysis of Drifting Dynamics with Neural Network Hidden Markov Models

Jens Kohlmorgen, Klaus-Robert Müller, Klaus Pawelzik

Abstract: We present a method for the analysis of nonstationary time se(cid:173) ries with multiple operating modes. In particular, it is possible to detect and to model both a switching of the dynamics and a less abrupt, time consuming drift from one mode to another. This is achieved in two steps. First, an unsupervised training method pro(cid:173) vides prediction experts for the inherent dynamical modes. Then, the trained experts are used in a hidden Markov model that allows to model drifts. An application to physiological wake/sleep data demonstrates that analysis and modeling of real-world time series can be improved when the drift paradigm is taken into account.