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Abstract:
A method for the analysis of nonstationary time series with
multiple operating modes is presented. In particular, it is
possible to detect and to model a switching of the dynamics and
also a less abrupt, time consuming drift from one mode to another.
This is achieved by an unsupervised algorithm that segments the
data according to inherent modes, and a subsequent search through
the space of possible 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. In the case of wake/sleep data, we hope to gain
more insight into the physiological processes that are involved in
the transition from wake to sleep.
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