Exact Discovery of Time Series Motifs

By Abdullah Mueen, Eamonn J. Keogh, Qiang Zhu, Sydney S. Cash, Brandon Westover. SDM, 2009

336 citations recorded by the source corpus.

Time series motifs are pairs of individual time series, or subsequences of a longer time series, which are very similar to each other. As with their discrete analogues in computational biology, this similarity hints at structure which has been conserved for some reason and may therefore be of interest. Since the formalism of time series motifs in 2002, dozens of researchers have used them for diverse applications in many different domains. Because the obvious algorithm for computing motifs is quadratic in the number of items, more than a dozen approximate algorithms to discover motifs have been proposed in the literature. In this work, for the first time, we show a tractable exact algorithm to find time series motifs. As we shall show through extensive experiments, our algorithm is up to three orders of magnitude faster than brute-force search in large datasets. We further show that our algorithm is fast enough to be used as a subroutine in higher level data mining algorithms for anytime classification, near-duplicate detection and summarization, and we consider detailed case studies in domains as diverse as electroencephalograph interpretation and entomological telemetry data mining.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b08b0a84e23f7c57d41e910a4e447a216085b740

Citations in the corpus

• Guidelines for the Treatment of Hypothyroidism: Prepared by the American Thyroid Association Task Force on Thyroid Hormone Replacement (2014, neutral): “For example, while excess T4 could in theory lead to increased T3 production in D2-expressing tissues, the regulation of D2 by the ubiquination-proteasomal system would tend to minimize this effect by inhibiting D2 activity and shortening its halflife (357).”