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Exploring the diverse world of SAX-based methodologies

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Abstract

Symbolic Aggregate Approximation (SAX) is a widely used method for time series data analysis, known for its ability to transform continuous data to discrete symbols. While SAX has demonstrated its effectiveness in various applications, it is not without limitations. SAX method has certain weaknesses that have motivated researchers to further investigate and propose new modifications to address them. In this article, we present a comprehensive review of the published variations of SAX and categorize them into groups based on the weaknesses they aim to overcome. This taxonomy provides a valuable resource for researchers interested in SAX, as it consolidates a wide range of modifications in one work. Through an extensive literature review, we have gathered a diverse collection of all published variations of the SAX method. Each variation is accompanied by a concise description, facilitating readers’ understanding of the key features and benefits of each approach. By systematically presenting these variations, this paper encourages the exploration of the SAX method’s landscape, enabling researchers to identify the most suitable method for their specific needs, revealing research gaps and alongside promoting further investigation in this field.

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Acknowledgements

This research has been financed by the European Union : Next Generation EU through the Program Greece 2.0 National Recovery and Resilience Plan, under the call RESEARCH – CREATE – INNOVATE, project name “iCREW: Intelligent small craft simulator for advanced crew training using Virtual Reality techniques" (project code: TAEDK-06195)

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  1. Department of Informatics and Telecommunications, University of Ioannina, 47150, Arta, Greece

    Lamprini Pappa, Petros Karvelis & Chrysostomos Stylios

  2. Industrial Systems Institute, Athena RC, 26504, Patras, Greece

    Chrysostomos Stylios

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  1. Lamprini Pappa
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Pappa, L., Karvelis, P. & Stylios, C. Exploring the diverse world of SAX-based methodologies. Data Min Knowl Disc 39, 4 (2025). https://doi.org/10.1007/s10618-024-01075-2

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