Abstract
Dynamic Time Warping (DTW) distance has been proven to work exceptionally well, but with higher time and space complexities. Particularly for time series data, subsequence matching under DTW distance poses a much challenging problem to work on streaming data. Recent work, SPRING, has introduced a solution to this problem with only linear time and space which makes subsequence matching on data stream become more and more practical. However, we will demonstrate that it may still give inaccurate results, and then propose a novel Accurate Subsequence Matching (ASM) algorithm that eliminates this discrepancy by using a global constraint and a scaling factor. We further demonstrate utilities of our work on a comprehensive set of experiments that guarantees an improvement in accuracy while maintaining the same time and space complexities.
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Niennattrakul, V., Wanichsan, D., Ratanamahatana, C.A. (2010). Accurate Subsequence Matching on Data Stream under Time Warping Distance. In: Theeramunkong, T., et al. New Frontiers in Applied Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14640-4_12
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DOI: https://doi.org/10.1007/978-3-642-14640-4_12
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