Efficient query filtering for streaming time series with applications to semisupervised learning of time series classifiers
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- 作者：Li Wei ; Eamonn Keogh ; Helga Van Herle ; Agenor Mafra-Neto and Russell J. Abbott
- 关键词：Time series ; Data mining ; Streams ; Monitoring ; Semisupervised learning
- 刊名：Knowledge and Information Systems
- 出版时间：April, 2007
- 出版年：2007
- 期刊代码：98_0219-1377
- 类别：cp
- 卷：11
- 期：3
- 页码：313-344
- 数据来源：sp
摘要
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.