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Accurate and fast time series classification based on compressed random Shapelet Forest

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Abstract

Achieving accurate, fast, and interpretable time series classification (TSC) has attracted considerable attention from the data mining community over the past decades. In this paper, we propose an efficient algorithm, called Compressed Random Shapelet Forest (CRSF), to tackle this problem. Different from most of the shapelet-based TSC methods, CRSF obtains promising performance by greatly compressing the shapelet features space. In order to achieve the aim of compression, the time series dataset, as well as the shapelets, are represented by Symbolic Aggregate approXimation (SAX) at first. Then, the shapelet-based decision trees are built upon a pool of high-quality shapelet candidates of which the useless shapelets and the self-similar shapelets have been pre-pruned. A new function for measuring the distance between two SAX-represented time series is also introduced. Extensive experiments were conducted on 50 UCR time series datasets. The results show that (1) CRSF can achieve the highest average accuracy on the datasets and it outperforms most of the existing shapelet-based TSC methods; (2) CRSF is slightly superior to gRSF in terms of accuracy and is significantly superior to gRSF in terms of time cost. Specifically, it is on average 41 times faster than gRSF according to the experimental results.

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Acknowledgements

The authors thank the reviewers for their work and the contributors of the UCR archive. This work is supported by the open project fund of Intelligent Terminal Key Laboratory of Sichuan Province (Grant No. SCITLAB-1002), and the open fund of Key Laboratory of Internet Natural Language Processing of Sichuan Province Education Department (Grant No. INLP201906), and fund of Science and Technology Bureau of Leshan Town (Grant Nos. 21SZD092, 20GZD020).

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Authors and Affiliations

  1. Chengdu Institute of Computer Application, Chinese Academy of Sciences, Chengdu, 610041, China

    Jun Yang

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Jun Yang

  3. School of Electronic Information and Artificial Intelligence, Leshan Normal University, Leshan, 614000, China

    Jun Yang, Siyuan Jing & Guanying Huang

  4. Intelligent Terminal Key Laboratory of Sichuan Province, Yibin, 644000, China

    Siyuan Jing

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  1. Jun Yang
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Correspondence to Siyuan Jing.

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Yang, J., Jing, S. & Huang, G. Accurate and fast time series classification based on compressed random Shapelet Forest. Appl Intell 53, 5240–5258 (2023). https://doi.org/10.1007/s10489-022-03852-2

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