A new approach for optimal offline time-series segmentation with error bound guarantee
Author
Carmona Poyato, Ángel
Fernández García, Nicolás Luis
Madrid-Cuevas, F.J.
Durán Rosal, Antonio Manuel
Publisher
ElsevierDate
2021Subject
Offline Time series segmentationFeasible Space
L∞-norm
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This paper proposes a new optimal approach, called OSFS, based on feasible space (FS) Liu et al. (2008)[, that minimizes the number of segments of the approximation and guarantees the error bound using the L∞-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 L∞-norm over L2-norm.