A Systematic Review of Symbolic Aggregate Approximation (SAX)

Mehmet Eren Nalici; İsmet Soylemez; Ramazan Ünlü

doi:10.31801/cfsuasmas.1698467

A Systematic Review of Symbolic Aggregate Approximation (SAX)

Abstract

Time series data can be analyzed through various techniques to tackle classification or regression tasks. Symbolic Aggregate Approximation (SAX) is one such technique used for time series data reduction that converts the data into a symbolic representation, enabling more efficient storage, retrieval, and analysis by reducing the dimensionality while preserving the essential patterns within the time series. In this paper, we provide a systematic literature review of SAX by examining relevant literature from 2007 to 2025. The review includes 321 articles sourced from the Web of Science (WOS) database. However, the 85 most cited and recently published studies are summarized. Utilizing collaboration network analysis, the study identifies the nations, affiliations, and authors involved in SAX research, as well as their co-authors and commonalities. Additionally, an analysis is conducted to explore the potential relationship between the articles and the United Nations' Sustainable Development Goals. These findings provide insights into the current landscape of SAX research and offer potential avenues for future exploration. By pinpointing research gaps, scholars can use this review to anticipate forthcoming research trajectories.

Keywords

References

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Details

Primary Language

English

Subjects

Statistical Data Science

Journal Section

Review Article

Authors

Mehmet Eren Nalici
0000-0002-7954-6916
Türkiye

İsmet Soylemez ^*
0000-0002-8253-9389
Türkiye

Ramazan Ünlü
0000-0002-1201-195X
Türkiye

Publication Date

March 29, 2026

Submission Date

May 13, 2025

Acceptance Date

August 23, 2025

Published in Issue

Year 1970 Volume: 75 Number: 1

DOI

https://doi.org/10.31801/cfsuasmas.1698467

IZ

https://izlik.org/JA22ZT85GX

Cite

RIS / Bibtex

APA

Nalici, M. E., Soylemez, İ., & Ünlü, R. (2026). A Systematic Review of Symbolic Aggregate Approximation (SAX). Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 75(1), 146-167. https://doi.org/10.31801/cfsuasmas.1698467

AMA

1.Nalici ME, Soylemez İ, Ünlü R. A Systematic Review of Symbolic Aggregate Approximation (SAX). Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2026;75(1):146-167. doi:10.31801/cfsuasmas.1698467

Chicago

Nalici, Mehmet Eren, İsmet Soylemez, and Ramazan Ünlü. 2026. “A Systematic Review of Symbolic Aggregate Approximation (SAX)”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 75 (1): 146-67. https://doi.org/10.31801/cfsuasmas.1698467.

EndNote

Nalici ME, Soylemez İ, Ünlü R (March 1, 2026) A Systematic Review of Symbolic Aggregate Approximation (SAX). Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 75 1 146–167.

IEEE

[1]M. E. Nalici, İ. Soylemez, and R. Ünlü, “A Systematic Review of Symbolic Aggregate Approximation (SAX)”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 75, no. 1, pp. 146–167, Mar. 2026, doi: 10.31801/cfsuasmas.1698467.

ISNAD

Nalici, Mehmet Eren - Soylemez, İsmet - Ünlü, Ramazan. “A Systematic Review of Symbolic Aggregate Approximation (SAX)”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 75/1 (March 1, 2026): 146-167. https://doi.org/10.31801/cfsuasmas.1698467.

JAMA

1.Nalici ME, Soylemez İ, Ünlü R. A Systematic Review of Symbolic Aggregate Approximation (SAX). Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2026;75:146–167.

MLA

Nalici, Mehmet Eren, et al. “A Systematic Review of Symbolic Aggregate Approximation (SAX)”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 75, no. 1, Mar. 2026, pp. 146-67, doi:10.31801/cfsuasmas.1698467.

Vancouver

1.Mehmet Eren Nalici, İsmet Soylemez, Ramazan Ünlü. A Systematic Review of Symbolic Aggregate Approximation (SAX). Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2026 Mar. 1;75(1):146-67. doi:10.31801/cfsuasmas.1698467