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A new approach for optimal offline time-series segmentation with error bound guarantee

Authors

Carmona-Poyato Á. , Fernández-Garcia N.L. , Madrid-Cuevas F.J. , DURAN ROSAL, ANTONIO MANUEL

External publication

No

Means

Pattern Recogn.

Scope

Article

Nature

Científica

JCR Quartile

SJR Quartile

JCR Impact

8.518

SJR Impact

3.113

Publication date

01/01/2021

ISI

000639744500001

Scopus Id

2-s2.0-85101905482

Abstract

Piecewise Linear Approximation is one of the most commonly used strategies to represent time series effectively and approximately. This approximation divides the time series into non-overlapping segments and approximates each segment with a straight line. Many suboptimal methods were proposed for this purpose. This paper proposes a new optimal approach, called OSFS, based on feasible space (FS) Liu et al. (2008)[1], that minimizes the number of segments of the approximation and guarantees the error bound using the L8-norm. On the other hand, a new performance measure combined with the OSFS method has been used to evaluate the performance of some suboptimal methods and that of the optimal method that minimizes the holistic approximation error (L2-norm). The results have shown that the OSFS method is optimal and demonstrates the advantages of L8-norm over L2-norm. © 2021 Elsevier Ltd

Keywords

Piecewise linear techniques; Time series; Approximation errors; New approaches; Optimal approaches; Optimal methods; Performance measure; Piecewise linear approximations; Sub-optimal method; Time-series segmentation; Errors

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