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Fast Time Series Classification with Random Symbolic Subsequences

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Advanced Analytics and Learning on Temporal Data (AALTD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13812))

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Abstract

Symbolic representations of time series have proven to be effective for time series classification, with many recent approaches including BOSS, WEASEL, and MrSEQL. These classifiers use various elaborate methods to select discriminative features from symbolic representations of time series. As a result, although they have competitive results regarding accuracy, their classification models are relatively expensive to train. Most if not all of these approaches have missed an important research question: are these elaborate feature selection methods actually necessary? ROCKET, a state-of-the-art time series classifier, outperforms all of them without utilizing any feature selection techniques. In this paper, we answer this question by contrasting these classifiers with a very simple method, named MrSQM. This method samples random subsequences from symbolic representations of time series. Our experiments on 112 datasets of the UEA/UCR benchmark demonstrate that MrSQM can quickly extract useful features and learn accurate classifiers with the logistic regression algorithm. MrSQM completes training and prediction on 112 datasets in 1.5 h for an accuracy comparable to existing efficient state-of-the-art methods, e.g., MrSEQL (10 h) and ROCKET (2.5 h). Furthermore, MrSQM enables the user to trade-off accuracy and speed by controlling the type and number of symbolic representations, thus further reducing the total runtime to 20 min for a similar level of accuracy. With these results, we show that random subsequences extracted from symbolic transformations can be as effective as the more sophisticated and expensive feature selection methods proposed in previous works. We propose MrSQM as a strong baseline for future research in time series classification, especially for approaches based on symbolic representations of time series.

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Notes

  1. 1.

    https://github.com/mlgig/mrsqm.

  2. 2.

    FFTW is an open source C library for efficiently computing the Discrete Fourier Transform (DFT): https://www.fftw.org.

  3. 3.

    https://github.com/mlgig/mrsqm.

  4. 4.

    https://timeseriesclassification.com.

  5. 5.

    https://www.sktime.org/en/stable/get_started.html.

  6. 6.

    https://github.com/b0rxa/scmamp.

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Acknowledgments

This publication has emanated from research supported in part by a grant from Science Foundation Ireland through the VistaMilk SFI Research Centre (SFI/16/RC/3835) and the Insight Centre for Data Analytics (12/RC/2289_P2).

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Nguyen, T.L., Ifrim, G. (2023). Fast Time Series Classification with Random Symbolic Subsequences. In: Guyet, T., Ifrim, G., Malinowski, S., Bagnall, A., Shafer, P., Lemaire, V. (eds) Advanced Analytics and Learning on Temporal Data. AALTD 2022. Lecture Notes in Computer Science(), vol 13812. Springer, Cham. https://doi.org/10.1007/978-3-031-24378-3_4

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  • DOI: https://doi.org/10.1007/978-3-031-24378-3_4

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