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    Approximate embedding-based subsequence matching of time series

    Athitsos, V. and Papapetrou, Panagiotis and Potamias, M. and Kollios, G. and Gunopulos, D. (2008) Approximate embedding-based subsequence matching of time series. In: Lakshmanan, L.V.S. and Ng, R.T. and Shasha, D. (eds.) Proceedings of the 2008 ACM SIGMOD international conference on Management of data. New York, USA: ACM Publications, pp. 365-378. ISBN 9781605581026.

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    A method for approximate subsequence matching is introduced, that significantly improves the efficiency of subsequence matching in large time series data sets under the dynamic time warping (DTW) distance measure. Our method is called EBSM, shorthand for Embedding-Based Subsequence Matching. The key idea is to convert subsequence matching to vector matching using an embedding. This embedding maps each database time series into a sequence of vectors, so that every step of every time series in the database is mapped to a vector. The embedding is computed by applying full dynamic time warping between reference objects and each database time series. At runtime, given a query object, an embedding of that object is computed in the same manner, by running dynamic time warping between the reference objects and the query. Comparing the embedding of the query with the database vectors is used to efficiently identify relatively few areas of interest in the database sequences. Those areas of interest are then fully explored using the exact DTW-based subsequence matching algorithm. Experiments on a large, public time series data set produce speedups of over one order of magnitude compared to brute-force search, with very small losses (< 1%) in retrieval accuracy.


    Item Type: Book Section
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Depositing User: Sarah Hall
    Date Deposited: 01 Aug 2013 10:49
    Last Modified: 09 Aug 2023 12:34


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