FINDING OR NOT FINDING RULES IN TIME SERIES

Jessica Lin and Eamonn Keogh

A chapter in Applications of Artificial Intelligence in Finance and Economics, 2004, pp 175-201 from Emerald Group Publishing Limited

Abstract: Given the recent explosion of interest in streaming data and online algorithms, clustering of time series subsequences has received much attention. In this work we make a surprising claim. Clustering of time series subsequences is completely meaningless. More concretely, clusters extracted from these time series are forced to obey a certain constraint that is pathologically unlikely to be satisfied by any dataset, and because of this, the clusters extracted by any clustering algorithm are essentially random. While this constraint can be intuitively demonstrated with a simple illustration and is simple to prove, it has never appeared in the literature. We can justify calling our claim surprising, since it invalidates the contribution of dozens of previously published papers. We will justify our claim with a theorem, illustrative examples, and a comprehensive set of experiments on reimplementations of previous work.

Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-9053(04)19007-5

DOI: 10.1016/S0731-9053(04)19007-5

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