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Open AccessArticle

SIMIT: Subjectively Interesting Motifs in Time Series

Department of Electronics and Information Systems, Ghent University, Technologiepark-Zwijnaarde 122, 9052 Ghent, Belgium
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Entropy 2019, 21(6), 566; https://doi.org/10.3390/e21060566
Received: 3 May 2019 / Revised: 25 May 2019 / Accepted: 3 June 2019 / Published: 5 June 2019
(This article belongs to the Special Issue Information-Theoretical Methods in Data Mining)
Numerical time series data are pervasive, originating from sources as diverse as wearable devices, medical equipment, to sensors in industrial plants. In many cases, time series contain interesting information in terms of subsequences that recur in approximate form, so-called motifs. Major open challenges in this area include how one can formalize the interestingness of such motifs and how the most interesting ones can be found. We introduce a novel approach that tackles these issues. We formalize the notion of such subsequence patterns in an intuitive manner and present an information-theoretic approach for quantifying their interestingness with respect to any prior expectation a user may have about the time series. The resulting interestingness measure is thus a subjective measure, enabling a user to find motifs that are truly interesting to them. Although finding the best motif appears computationally intractable, we develop relaxations and a branch-and-bound approach implemented in a constraint programming solver. As shown in experiments on synthetic data and two real-world datasets, this enables us to mine interesting patterns in small or mid-sized time series. View Full-Text
Keywords: time series; motif detection; information theory; subjective interestingness; pattern mining; exploratory data mining time series; motif detection; information theory; subjective interestingness; pattern mining; exploratory data mining
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Deng, J.; Lijffijt, J.; Kang, B.; De Bie, T. SIMIT: Subjectively Interesting Motifs in Time Series. Entropy 2019, 21, 566.

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