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Tri-Partition State Alphabet-Based Sequential Pattern for Multivariate Time Series

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Abstract

Recently, the advancement of cognitive computing and three-way decisions has enabled in-depth sequential pattern understanding through temporal association analysis. The main challenge is to obtain concise patterns that express richer semantics for multivariate time series (MTS) analysis. In this paper, we propose a tri-partition state alphabet-based sequential pattern (Tri-SASP) for MTSs. First, a tri-wildcard gap inserted between each pair of adjacent states enhances the flexibility of the method. Second, a given set of states is partitioned into positive (POS), negative (NEG) and boundary (BND) regions. The states in POS can only be used to construct a Tri-SASP, the states in NEG can only be matched by a tri-wildcard gap, and the states in BND can be used in both ways. Finally, horizontal and vertical algorithms are proposed to obtain frequent Tri-SASPs in a breadth-first manner. The experimental results on four real-world datasets show that (1) the discovered Tri-SASPs and temporal rules can enrich human cognition; (2) the two tri-partition strategies can bring us very meaningful and varied Tri-SASPs; and (3) the two algorithms are effective and scalable.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets/Air+Quality

  2. http://www.tipdm.org/bdrace/index.html

  3. http://www.cnooc.com.cn/en/

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Funding

This study was funded by the National Natural Science Foundation of China (grant numbers 41604114, 62006200); the Sichuan Science and Technology Program (grant numbers 2019YFG0216, 2020YFG0307); the Scientific Research and Innovation Team of Sichuan Tourism University (grant number 18SCTUTD06); and the Scientific Research Project of Sichuan Tourism University (grant number 2020SCTU14).

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Authors and Affiliations

  1. School of Information and Engineering, Sichuan Tourism University, No. 459 Hongling Road, Longquanyi District, Chengdu City, Sichuan Province, 610100, China

    Zhi-Heng Zhang, Gong-Suo Chen, Shao-Peng Shen, Zuo-Cheng Wen & Xiang-Bing Zhou

  2. School of Computer Science, Southwest Petroleum University, No. 8 Xindu Avenue, Xindu District, Chengdu City, Sichuan Province, 610500, China

    Fan Min

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  1. Zhi-Heng Zhang
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Correspondence to Fan Min.

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Zhang, ZH., Min, F., Chen, GS. et al. Tri-Partition State Alphabet-Based Sequential Pattern for Multivariate Time Series. Cogn Comput 14, 1881–1899 (2022). https://doi.org/10.1007/s12559-021-09871-4

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