Title Simultaneous optimisation of clustering quality and approximation error for time series segmentation
Authors DURAN ROSAL, ANTONIO MANUEL, Antonio Gutierrez, Pedro , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, Hervas-Martinez, Cesar
External publication No
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 5.524
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042406431&doi=10.1016%2fj.ins.2018.02.041&partnerID=40&md5=95c09bb371f46f037cfe4b3a6b7b4749
Publication date 01/05/2018
ISI 000428827300012
Scopus Id 2-s2.0-85042406431
DOI 10.1016/j.ins.2018.02.041
Abstract Time series segmentation is aimed at representing a time series by using a set of segments. Some researchers perform segmentation by approximating each segment with a simple model (e.g. a linear interpolation), while others focus their efforts on obtaining homogeneous groups of segments, so that common patterns or behaviours can be detected. The main hypothesis of this paper is that both objectives are conflicting, so time series segmentation is proposed to be tackled from a multiobjective perspective, where both objectives are simultaneously considered, and the expert can choose the desired solution from a Pareto Front of different segmentations. A specific multiobjective evolutionary algorithm is designed for the purpose of deciding the cut points of the segments, integrating a clustering algorithm for fitness evaluation. The experimental validation of the methodology includes three synthetic time series and three time series from real-world problems. Nine clustering quality assessment metrics are experimentally compared to decide the most suitable one for the algorithm. The proposed algorithm shows good performance for both clustering quality and reconstruction error, improving the results of other mono-objective alternatives of the state-of-the-art and showing better results than a simple weighted linear combination of both corresponding fitness functions. (C) 2018 Elsevier Inc. All rights reserved.
Keywords Time series segmentation; Multiobjective optimisation; Clustering; Evolutionary computation
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