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Side-Length-Independent Motif ( SLIM ): Motif Discovery and Volatility Analysis in Time Series— SAX , MDL and the Matrix Profile

Author

Listed:
  • Eoin Cartwright

    (Modelling & Scientific Computing Group (ModSci), School of Computing, Dublin City University, D09Y074 Dublin, Ireland
    These authors contributed equally to this work.)

  • Martin Crane

    (ADAPT Centre, School of Computing, Dublin City University, D09Y074 Dublin, Ireland
    These authors contributed equally to this work.)

  • Heather J. Ruskin

    (Modelling & Scientific Computing Group (ModSci), School of Computing, Dublin City University, D09Y074 Dublin, Ireland
    These authors contributed equally to this work.)

Abstract
As the availability of big data-sets becomes more widespread so the importance of motif (or repeated pattern) identification and analysis increases. To date, the majority of motif identification algorithms that permit flexibility of sub-sequence length do so over a given range, with the restriction that both sides of an identified sub-sequence pair are of equal length. In this article, motivated by a better localised representation of variations in time series, a novel approach to the identification of motifs is discussed, which allows for some flexibility in side-length. The advantages of this flexibility include improved recognition of localised similar behaviour (manifested as motif shape ) over varying timescales. As well as facilitating improved interpretation of localised volatility patterns and a visual comparison of relative volatility levels of series at a globalised level. The process described extends and modifies established techniques, namely SAX , MDL and the Matrix Profile, allowing advantageous properties of leading algorithms for data analysis and dimensionality reduction to be incorporated and future-proofed. Although this technique is potentially applicable to any time series analysis, the focus here is financial and energy sector applications where real-world examples examining S&P500 and Open Power System Data are also provided for illustration.

Suggested Citation

  • Eoin Cartwright & Martin Crane & Heather J. Ruskin, 2022. "Side-Length-Independent Motif ( SLIM ): Motif Discovery and Volatility Analysis in Time Series— SAX , MDL and the Matrix Profile," Forecasting, MDPI, vol. 4(1), pages 1-19, February.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:1:p:13-237:d:742303
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    References listed on IDEAS

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