GONG Xudong, WANG Caimei, XIONG Yan, HUANG Wenchao. Similar Time Series Retrieval Using Only Important Segments[J]. Chinese Journal of Electronics, 2017, 26(1): 22-26. doi: 10.1049/cje.2016.08.005
Citation: GONG Xudong, WANG Caimei, XIONG Yan, HUANG Wenchao. Similar Time Series Retrieval Using Only Important Segments[J]. Chinese Journal of Electronics, 2017, 26(1): 22-26. doi: 10.1049/cje.2016.08.005

Similar Time Series Retrieval Using Only Important Segments

doi: 10.1049/cje.2016.08.005
Funds:  This work is supported by the National Natural Science Foundation of China (No.61202404, No.61170233, No.61232018, No.61272472, No.61272317), and the Fundamental Research Funds for the Central Universities (No.WK0110000041).
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  • Corresponding author: WANG Caimei (corresponding author) was born in 1978. She is a lecturer in Department of Computer Science and Technology, HeFei University. Her main research interests include computer network, information security, and mobile computation. (Email:wangcmo@mail.ustc.edu.cn)
  • Received Date: 2014-12-30
  • Rev Recd Date: 2015-01-28
  • Publish Date: 2017-01-10
  • Similar time series searching plays an important role in applications such as time series classification and outlier detection. We observe that different segment of a time series may have different significance, thus propose to assign different weight to each segment, and extract those segments with highest weights for distance computation. Since these segments are more representative, we can achieve high accuracy of similarity search with much lower computation overhead. The result of experiments on both real world and synthetic data sets demonstrates that we can achieve comparable or even higher accuracy while largely reduce the computation overhead, if we use only those important segments rather than the whole time series while performing similarity search.
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