Time Series Segmentation of Paleoclimate Tipping Points by an Evolutionary Algorithm

• Published in: Hybrid Artificial Intelligence Systems pp. 318 - 329
• Main Authors: Pérez-Ortiz, M., Gutiérrez, P. A., Sánchez-Monedero, J., Hervás-Martínez, C., Nikolaou, Athanasia, Dicaire, Isabelle, Fernández-Navarro, Francisco
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: abrupt climate change; clustering; genetic algorithms; paleoclimate data; Time series segmentation; tipping points
• ISBN: 9783319076164; 3319076167
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-07617-1_29

Recent studies propose that some dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TP). 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 segmentation of a paleoclimate time series to find segments sharing common patterns with the purpose of finding one or more kinds of segments corresponding to 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 perform time series segmentation comparisons. Without a priori information, the method clusters together most of the TPs and avoids false positives, which is a promising result given the challenging nature of the problem.