Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers

Authors:
Li Wei;Eamonn Keogh;Helga Van Herle;Agenor Mafra-Neto;Russell J. Abbott
Affiliations:
University of California – Riverside, Computer Science and Engineering Department, 90032, Riverside, CA, USA;University of California – Riverside, Computer Science and Engineering Department, 90032, Riverside, CA, USA;University of California – Los Angeles, David Geffen School of Medicine, 90032, Los Angeles, CA, USA;ISCA Technologies, 90032, Riverside, CA, USA;The Aerospace Corporation, 90032, El Segundo, CA, USA
Venue:
Knowledge and Information Systems
Year:
2007

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Abstract

In this paper, we define time series query filtering, the problem of monitoring the streaming time series for a set of predefined patterns. This problem is of great practical importance given the massive volume of streaming time series available through sensors, medical patient records, financial indices and space telemetry. Since the data may arrive at a high rate and the number of predefined patterns can be relatively large, it may be impossible for the comparison algorithm to keep up. We propose a novel technique that exploits the commonality among the predefined patterns to allow monitoring at higher bandwidths, while maintaining a guarantee of no false dismissals. Our approach is based on the widely used envelope-based lower-bounding technique. As we will demonstrate on extensive experiments in diverse domains, our approach achieves tremendous improvements in performance in the offline case, and significant improvements in the fastest possible arrival rate of the data stream that can be processed with guaranteed no false dismissals. As a further demonstration of the utility of our approach, we demonstrate that it can make semisupervised learning of time series classifiers tractable.