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2012
ACM

Searching and mining trillions of time series subsequences under dynamic time warping

7 years 2 months ago
Searching and mining trillions of time series subsequences under dynamic time warping
Most time series data mining algorithms use similarity search as a core subroutine, and thus the time taken for similarity search is the bottleneck for virtually all time series data mining algorithms. The difficulty of scaling search to large datasets largely explains why most academic work on time series data mining has plateaued at considering a few millions of time series objects, while much of industry and science sits on billions of time series objects waiting to be explored. In this work we show that by using a combination of four novel ideas we can search and mine truly massive time series for the first time. We demonstrate the following extremely unintuitive fact; in large datasets we can exactly search under DTW much more quickly than the current state-of-the-art Euclidean distance search algorithms. We demonstrate our work on the largest set of time series experiments ever attempted. In particular, the largest dataset we consider is larger than the combined size of all of t...
Thanawin Rakthanmanon, Bilson J. L. Campana, Abdul
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where KDD
Authors Thanawin Rakthanmanon, Bilson J. L. Campana, Abdullah Mueen, Gustavo E. A. P. A. Batista, M. Brandon Westover, Qiang Zhu 0002, Jesin Zakaria, Eamonn J. Keogh
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