Abstract
Suppose one has a set of univariate time series generated by one or more unknown processes. The problem we wish to solve is to discover the most probable set of processes generating the data by clustering time series into groups so that the elements of each group have similar dynamics. For example, if a batch of time series represents sensory experiences of a mobile robot, clustering by dynamics might find clusters corresponding to abstractions of sensory inputs (Ramoni, Sebastiani, Cohen, Warwick, & Davis, 1999).
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Sebastiani, P., Ramoni, M., Cohen, P. (2000). Sequence Learning via Bayesian Clustering by Dynamics. In: Sun, R., Giles, C.L. (eds) Sequence Learning. Lecture Notes in Computer Science(), vol 1828. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44565-X_2
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DOI: https://doi.org/10.1007/3-540-44565-X_2
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