Abstract
This paper presents a local search (LS) method based on the beta distribution for time series segmentation with the purpose of correctly representing extreme values of the underlying variable studied. The LS procedure is combined with an evolutionary algorithm (EA) which segments time series trying to obtain a given number of homogeneous groups of segments. The proposal is tested on a real problem of wave height estimation, where extreme high waves are frequently found. The results show that the LS is able to significantly improve the clustering quality of the solutions obtained by the EA. Moreover, the best segmentation clearly groups extreme waves in a separate cluster and characterizes them according to their centroid.
This work has been subsidized by the project TIN2014-54583-C2-1-R of the Spanish Ministry of Economy and Competitiveness (MINECO), FEDER funds and the P11-TIC-7508 project of the Junta de Andalucía (Spain). Antonio M. Durán-Rosal’s research has been subsidized by the FPU Predoctoral Program of the Spanish Ministry of Education, Culture and Sport (MECD), grant reference FPU14/03039.
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Durán-Rosal, A.M., Dorado-Moreno, M., Gutiérrez, P.A., Hervás-Martínez, C. (2016). On the Use of the Beta Distribution for a Hybrid Time Series Segmentation Algorithm. In: Luaces , O., et al. Advances in Artificial Intelligence. CAEPIA 2016. Lecture Notes in Computer Science(), vol 9868. Springer, Cham. https://doi.org/10.1007/978-3-319-44636-3_39
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