Fast Constrained Dynamic Time Warping for Similarity Measure of Time Series Data
نویسندگان
چکیده
منابع مشابه
Time-series averaging using constrained dynamic time warping with tolerance
In this paper, we propose an innovative averaging of a set of time-series based on the Dynamic Time Warping (DTW). The DTW is widely used in data mining since it provides not only a similarity measure, but also a temporal alignment of time-series. However, its use is often restricted to the case of a pair of signals. In this paper, we propose to extend its application to a set of signals by pro...
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Dynamic Time Warping (DTW) distance has been effectively used in mining time series data in a multitude of domains. However, DTW, in its original formulation, is extremely inefficient in comparing long sparse time series, which mostly contain zeros and unevenly spaced non-zero observations. Original DTW distance does not take advantage of the sparsity, and thus, incur a prohibitively large comp...
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Time series are a ubiquitous form of data occurring in virtually every scientific discipline and business application. There has been much recent work on adapting data mining algorithms to time series databases. For example, Das et al. attempt to show how association rules can be learned from time series [7]. Debregeas and Hebrail [8] demonstrate a technique for scaling up time series clusterin...
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Dynamic time warping (DTW), which finds the minimum path by providing non-linear alignments between two time series, has been widely used as a distance measure for time series classification and clustering. However, DTW does not account for the relative importance regarding the phase difference between a reference point and a testing point. Thismay lead tomisclassification especially in applica...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3043839