Time series data appears in numerous applications including medical data processing, financial analytics, network traffic monitoring, and Web click-stream analysis. An essential task in time series mining is efficiently finding matches between similar time series or parts of time series in a large dataset. In this work, we introduce a new definition of subseries join as a generalization of subseries matching. We then propose an efficient and robust solution to subseries join (and match) based on a non-uniform segmentation and a hierarchical feature representation. Experiments demonstrate the effectiveness of our approach and also show that this approach can better tolerate noise and phase-scaling than previous work. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Lin, Y., & McCool, M. D. (2010). Subseries join: A similarity-based time series match approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6118 LNAI, pp. 238–245). https://doi.org/10.1007/978-3-642-13657-3_27
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