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Distance measure with improved lower bound for multivariate time series

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  • Li, Hailin

Abstract

Lower bound function is one of the important techniques used to fast search and index time series data. Multivariate time series has two aspects of high dimensionality including the time-based dimension and the variable-based dimension. Due to the influence of variable-based dimension, a novel method is proposed to deal with the lower bound distance computation for multivariate time series. The proposed method like the traditional ones also reduces the dimensionality of time series in its first step and thus does not directly apply the lower bound function on the multivariate time series. The dimensionality reduction is that multivariate time series is reduced to univariate time series denoted as center sequences according to the principle of piecewise aggregate approximation. In addition, an extended lower bound function is designed to obtain good tightness and fast measure the distance between any two center sequences. The experimental results demonstrate that the proposed lower bound function has better tightness and improves the performance of similarity search in multivariate time series datasets.

Suggested Citation

  • Li, Hailin, 2017. "Distance measure with improved lower bound for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 622-637.
    • Handle: RePEc:eee:phsmap:v:468:y:2017:i:c:p:622-637
    DOI: 10.1016/j.physa.2016.10.062
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    References listed on IDEAS

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    1. Li, Hailin, 2015. "Piecewise aggregate representations and lower-bound distance functions for multivariate time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 10-25.
    2. Qiang Yang & Xindong Wu, 2006. "10 Challenging Problems In Data Mining Research," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 597-604.
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