Cited By
View all- Gu KDuan XQi MYan L(2022)A global relative similarity for inferring interactions of multi-agent systemsComplex & Intelligent Systems10.1007/s40747-022-00877-59:2(1671-1686)Online publication date: 4-Oct-2022
Similarity Measure of Multivariate Time Series Based on Segmentation
Pages 47 - 51
Abstract
Similarity measure of time series is a fundamental problem in data mining tasks. However, most of the similarity methods are mainly for univariate time series, rather than multivariate time series. Among the existing approaches for multivariate time series, dynamic time warping can obtain high accuracy, but the calculation cost is expensive. To solve this challenging problem, a similarity measure of multivariate time series is proposed. We first segment multivariate time series and extract the mean value and span of each sequence as its feature. Then, a similarity measure based on dynamic time warping is proposed. Finally, extensive experiments on real-world data sets are executed. The experimental results indicate the proposed method can improve the efficiency while keeping the accuracy of similarity measure.
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Index Terms
- Similarity Measure of Multivariate Time Series Based on Segmentation
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Published In
February 2020
607 pages
ISBN:9781450376426
DOI:10.1145/3383972
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- Shenzhen University: Shenzhen University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 May 2020
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- Research-article
- Research
- Refereed limited
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- Natural Science Foundation of China
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ICMLC 2020
ICMLC 2020: 2020 12th International Conference on Machine Learning and Computing
February 15 - 17, 2020
Shenzhen, China
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Cited By
View all- Gu KDuan XQi MYan L(2022)A global relative similarity for inferring interactions of multi-agent systemsComplex & Intelligent Systems10.1007/s40747-022-00877-59:2(1671-1686)Online publication date: 4-Oct-2022
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