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  • A Grid-Based Index Method for Time Warping Distance

    Author(s)
    An, Jiyuan
    Chen, Yi-Ping Phoebe
    Keogh, Eamonn
    Griffith University Author(s)
    An, Jay
    Year published
    2004
    Metadata
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    Abstract
    Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree structures are quite weak, resulting in inefficient search. In this paper, we propose a dimensionality reduction method motivated by observations about the inherent character of each time series. A very compact index file is constructed. ...
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    Recently DTW (dynamic time warping) has been recognized as the most robust distance function to measure the similarity between two time series, and this fact has spawned a flurry of research on this topic. Most indexing methods proposed for DTW are based on the R-tree structure. Because of high dimensionality and loose lower bounds for time warping distance, the pruning power of these tree structures are quite weak, resulting in inefficient search. In this paper, we propose a dimensionality reduction method motivated by observations about the inherent character of each time series. A very compact index file is constructed. By scanning the index file, we can get a very small candidate set, so that the number of page access is dramatically reduced. We demonstrate the effectiveness of our approach on real and synthetic datasets
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    Journal Title
    Lecture Notes in Computer Science
    Volume
    3129
    Publication URI
    http://hdl.handle.net/10072/25668
    Collection
    • Journal articles

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