Title On the use of evolutionary time series analysis for segmenting paleoclimate data
Authors Perez-Ortiz, M., DURAN ROSAL, ANTONIO MANUEL, Gutierrez, P. A., SÁNCHEZ MONEDERO, JAVIER, Nikolaou, A., FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, Hervas-Martinez, C., FERNÁNDEZ NAVARRO, FRANCISCO DE ASÍS, SÁNCHEZ MONEDERO, JAVIER, DURAN ROSAL, ANTONIO MANUEL
External publication No
Means Neurocomputing
Scope Article
Nature Científica
JCR Quartile 1
SJR Quartile 1
JCR Impact 4.07200
Area International
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030174993&doi=10.1016%2fj.neucom.2016.11.101&partnerID=40&md5=d7cd724247e365c63916d6e7e90d73a1
Publication date 31/01/2018
ISI 000452306700002
Scopus Id 2-s2.0-85030174993
DOI 10.1016/j.neucom.2016.11.101
Abstract Recent studies propose that different dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TPs). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the development of a time series segmentation algorithm for paleoclimate data in order to find segments sharing common statistical patterns. The proposed algorithm uses a clustering-based approach for evaluating the solutions and six statistical features, most of which have been previously considered in the detection of early warning signals in paleoclimate TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series, together with a method to compare the segmentations. The final segments provided by the algorithm are used to construct a prediction model, whose promising results show the importance of segmentation for improving the understanding of a time series. (C) 2017 Published by Elsevier By.
Keywords Time series segmentation; Genetic algorithms; Clustering; Paleoclimate data; Tipping points; Abrupt climate change
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