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Abstract:
We propose a novel method for the analysis of sequential data
that exhibits an inherent mode switching. In particular, the data
might be a non-stationary time series from a dynamical system
that switches between multiple operating modes. Unlike other
approaches, our method processes the data incrementally and
without any training of internal parameters. We use an HMM with a
dynamically changing number of states and an on-line variant of
the Viterbi algorithm that performs an unsupervised segmentation
and classification of the data on-the-fly, i.e. the method is
able to process incoming data in real-time. The main idea of the
approach is to track and segment changes of the probability
density of the data in a sliding window on the incoming data
stream. The usefulness of the algorithm is demonstrated by an
application to a switching dynamical system.
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