Approximations to Magic: Finding Unusual Medical Time Series

Authors:
Jessica Lin;Eamonn Keogh;Ada Fu;Helga Van Herle
Affiliations:
University of California at Riverside;University of California at Riverside;Chinese University of Hong Kong;University of California at Los Angeles
Venue:
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Year:
2005

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Abstract

In this work we introduce the new problem of finding time series discords. Time series discords are subsequences of longer time series that are maximally different to all the rest of the time series subsequences. They thus capture the sense of the most unusual subsequence within a time series. While the brute force algorithm to discover time series discords is quadratic in the length of the time series, we show a simple algorithm that is 3 to 4 orders of magnitude faster than brute force, while guaranteed to produce identical results.