Lim, Hyunki Choi, Heeseung Choi, Yeji Kim, Ig-Jae 2024-01-19T16:32:27Z 2024-01-19T16:32:27Z 2021-09-02 2020-10 0167-8655 https://pubs.kist.re.kr/handle/201004/118062 In recent years, analyzing time-series data has become an ever important research topic due to an increased number of temporal datasets in science and engineering. Segmentation is widely used in time-series data analysis because it provides a more compact representation by dividing the series into segments. Segmentation approaches based on genetic algorithms have been proposed to extract segments and patterns with a given objective, such as a low rate of change or periodicity from time-series data. However, they may not be effective in obtaining the precise solution because they perform global search. In this study, we propose a memetic algorithm for multivariate time-series segmentation. For efficient local refinement, we calculate a likelihood-based score for all time points and use it in the evolutionary process. Experiments demonstrate that the proposed method is superior to conventional segmentation methods. The source code of the proposed method can be downloaded from https://github.com/hlim-kist/ma_mts (C) 2020 Elsevier B.V. All rights reserved. English ELSEVIER CLASSIFICATION OPTIMIZATION Memetic algorithm for multivariate time-series segmentation Article 10.1016/j.patrec.2020.06.022 1 PATTERN RECOGNITION LETTERS, v.138, pp.60 - 67 PATTERN RECOGNITION LETTERS 138 60 67 scie scopus 000579804900009 2-s2.0-85087658272 Computer Science, Artificial Intelligence Computer Science Article CLASSIFICATION OPTIMIZATION Time series segmentation Multivariate data Memetic algorithm