Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings

Research Article

An Efficient Method for Estimating Time Series Motif Length Using Sequitur Algorithm

  • @INPROCEEDINGS{10.1007/978-3-030-00557-3_52,
        author={Nguyen Phien},
        title={An Efficient Method for Estimating Time Series Motif Length Using Sequitur Algorithm},
        proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings},
        proceedings_a={MLICOM},
        year={2018},
        month={10},
        keywords={Motif detection Sequitur algorithm Grammar inference Motif length Time series},
        doi={10.1007/978-3-030-00557-3_52}
    }
    
  • Nguyen Phien
    Year: 2018
    An Efficient Method for Estimating Time Series Motif Length Using Sequitur Algorithm
    MLICOM
    Springer
    DOI: 10.1007/978-3-030-00557-3_52
Nguyen Phien,*
    *Contact email: nguyenngocphien@tdtu.edu.vn

    Abstract

    Motifs in time series are approximately repeated subsequence found within a long time series data. There are some popular and effective algorithms for finding motif in time series. However, these algorithms still have one major weakness: users of these algorithms are required to select an appropriate value of the motif length which is unknown in advance. In this paper, we propose a novel method to estimate the length of 1-motif in a time series. This method is based on GrammarViz, a variable-length motif detection approach which has Sequitur at its core. Sequitur is known as a grammar compression algorithm that is able to have enough identification not just common subsequences but also identify the hierarchical structure in data. As GrammarViz, our method is also based on the Sequitur algorithm, but for another purpose: a preprocessing step for finding motif in time series. The experimental results prove that our method can help to estimate very fast the length of 1-motif for some TSMD algorithms, such as Random Projection.