@article{Duran2021a,
author = "{\'A}ngel Carmona-Poyato and Nicolas Luis Fern{\'a}ndez-Garc{\'i}a and Francisco Jos{\'e} Madrid-Cuevas and Antonio Manuel Dur{\'a}n-Rosal",
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) [1], that minimizes the number of segments of the approximation and guarantees the error bound using the -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 (-norm). The results have shown that the OSFS method is optimal and demonstrates the advantages of -norm over -norm.",
awards = "JCR(2021): 8.518 Position: 22/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
comments = "JCR(2021): 8.518 Position: 22/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
doi = "10.1016/j.patcog.2021.107917",
issn = "0031-3203",
journal = "Pattern Recognition",
keywords = "Data representation, optimal time series segmentation, error bound guarantee, L-norm",
month = "July",
note = "JCR(2021): 8.518 Position: 22/144 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE",
pages = "107917",
title = "{A} new approach for optimal offline time-series segmentation with error bound guarantee",
url = "doi.org/10.1016/j.patcog.2021.107917",
volume = "115",
year = "2021",
}