Prefix-querying with an L1 distance metric for time-series subsequence matching under time warping

This paper discusses the way of processing time-series subsequence matching under time warping. Time warping enables sequences to be found with similar patterns even when they are of different lengths. Theprefix-querying method is the first index-based approach that efficiently performs time-series subsequence matching under time warping without false dismissals. This method employs theL distance metric as a base distance function so as to allow users to issue queries conveniently. In this paper, we extend the prefix-querying method for absorbingL 1, which is the most widely used as a base distance function in time-series subsequence matching under time warping, instead ofL. We formally prove that the prefix-querying method with theL 1 distance metric does not incur any false dismissals in the subsequence matching. To show its superiority, we conduct performance evaluation via a variety of experiments. The results reveal that our method achieves significant performance improvement over the previous methods, up to 10.7 times, with a data set containing real-world Korean stock data sequences, and up to 180 times with data sets containing a very large volume of synthetic data sequences.

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