Paper Info
Title
On the use of evolutionary time series analysis for segmenting paleoclimate data.
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.
Year
DOI
Venue
2019
10.1016/j.neucom.2016.11.101
Neurocomputing
Keywords
Field
DocType
Time series segmentation,Genetic algorithms,Clustering,Paleoclimate data,Tipping points,Abrupt climate change
Warning system,Time series,Data mining,Time-series segmentation,Market segmentation,Segmentation,Dynamical systems theory,Artificial intelligence,Cluster analysis,Genetic algorithm,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
326
0925-2312
2
PageRank 
References 
Authors
0.38
16
7
Name
Order
Citations
PageRank
María Pérez-Ortiz142.42
Antonio Manuel Durán-Rosal2145.36
Pedro Antonio Gutiérrez343347.30
Javier Sánchez-Monedero49812.18
Athanasia Nikolaou530.74
Francisco Fernández-Navarro624517.00
César Hervás-Martínez779678.92