Abstract
A method for the analysis of nonstationary time series with multiple modes of behaviour is presented. In particular, it is not only possible to detect a switching of dynamics but also a less abrupt, time consuming drift from one mode to another. This is achieved by an unsupervised algorithm for segmenting the data according to the modes and a subsequent search through the space of possible drifts. Applications to speech and physiological data demonstrate that analysis and modeling of real world time series can be improved when the drift paradigm is taken into account.
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CITATION STYLE
Kohlmorgen, J., Müller, K. R., & Pawelzik, K. (1996). Analysis of drifting dynamics with competing predictors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 785–790). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_132
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