Article: A novel shape-based time series classification with SAX-Ensemble Journal: International Journal of Computer Applications in Technology (IJCAT) 2023 Vol.71 No.1 pp.64 - 77 Abstract: Since the first publication of the Symbolic-Aggregate Approximation (SAX), a lot of extensions with novel SAX-distance measure are published. Each of them attempts to integrate additional statistical features in order to improve original SAX average-based feature. Each SAX-feature has its own distance function which quantifies the (dis)similarity between two Time Series (TS). However, none of them can fit the overall shape-characteristics of a TS and give the superiority to an individual SAX-based classifier. In order to combine the prediction of each single SAX-based classifier, we propose a collection of several SAX-features to compose a shape-based ensemble for TS classification. The proposed SAX-Ensemble scheme is applied on a multiple domain representation of the TS where the diversity of collected SAX-features make the setting of the SAX-discretisation parameters a challenging task especially for a long TS data or a large training data set. In order to avoid a time-consuming of either grid search or expensive optimisation algorithm, we instead apply a data-aware or data-agnostic parameters setting technique. Experimental results on real TS database show that the performance of the proposed SAX-Ensemble with data-aware technique exceeded the SAX-based classifiers with more flexible and realistic parameters estimation. Inderscience Publishers - linking academia, business and industry through research

Title: A novel shape-based time series classification with SAX-Ensemble

Authors: Mariem Taktak; Slim Triki

Addresses: Higher Institute of Applied Sciences and Technologies of Sousse, Sousse, Tunisia ' National Engineering School of Sfax, Sfax, Tunisia

Abstract: Since the first publication of the Symbolic-Aggregate Approximation (SAX), a lot of extensions with novel SAX-distance measure are published. Each of them attempts to integrate additional statistical features in order to improve original SAX average-based feature. Each SAX-feature has its own distance function which quantifies the (dis)similarity between two Time Series (TS). However, none of them can fit the overall shape-characteristics of a TS and give the superiority to an individual SAX-based classifier. In order to combine the prediction of each single SAX-based classifier, we propose a collection of several SAX-features to compose a shape-based ensemble for TS classification. The proposed SAX-Ensemble scheme is applied on a multiple domain representation of the TS where the diversity of collected SAX-features make the setting of the SAX-discretisation parameters a challenging task especially for a long TS data or a large training data set. In order to avoid a time-consuming of either grid search or expensive optimisation algorithm, we instead apply a data-aware or data-agnostic parameters setting technique. Experimental results on real TS database show that the performance of the proposed SAX-Ensemble with data-aware technique exceeded the SAX-based classifiers with more flexible and realistic parameters estimation.

Keywords: time series data; symbolic aggregate approximation; shape-based classification.

DOI: 10.1504/IJCAT.2023.131065

International Journal of Computer Applications in Technology, 2023 Vol.71 No.1, pp.64 - 77

Received: 18 Dec 2021
Received in revised form: 04 May 2022
Accepted: 10 Jun 2022
Published online: 23 May 2023 *

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Title: A novel shape-based time series classification with SAX-Ensemble

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