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

Eoin Cartwright, Martin Crane and Heather J. Ruskin
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Eoin Cartwright: Modelling & Scientific Computing Group (ModSci), School of Computing, Dublin City University, D09Y074 Dublin, Ireland
Martin Crane: ADAPT Centre, School of Computing, Dublin City University, D09Y074 Dublin, Ireland
Heather J. Ruskin: Modelling & Scientific Computing Group (ModSci), School of Computing, Dublin City University, D09Y074 Dublin, Ireland

Forecasting, 2022, vol. 4, issue 1, 1-19

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.

Keywords: financial time series; matrix profile; symbolic aggregate approximation ( SAX ); minimum description length ( MDL ); time series motifs (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
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