Time series segmentation of paleoclimate tipping points by an evolutionary algorithm

Áreas de investigación:

• Methodology - Machine learning

Año:

2014

Tipo de publicación:

Artículo en conferencia

Palabras clave:

Time series segmentation, genetic algorithms, clustering, paleoclimate data, tipping points, abrupt climate change

Autores:

• Pérez-Ortiz, María
• Gutiérrez, Pedro Antonio
• Sánchez-Monedero, Javier
• Hervás-Martínez, César
• Nikolaou, Athanasia
• Dicaire, Isabelle
• Fernandez-Navarro, Francisco

Volumen:

8480

Título del libro:

9th International Conference on Hybrid Artificial Intelligence Systems (HAIS2014)

Serie:

Lecture Notes in Computer Science

Páginas:

318-329

Organización:

Salamanca (Spain)

Mes:

11th-13th September

ISBN:

978-3-319-07616-4

ISSN:

0302-9743

BibTex:

@conference{TSSofPTPsHAIS, author = "Mar{\'i}a P{\'e}rez-Ortiz and Pedro Antonio Guti{\'e}rrez and Javier S{\'a}nchez-Monedero and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Athanasia Nikolaou and Isabelle Dicaire and Fernandez-Navarro, Francisco", abstract = "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.", booktitle = "9th International Conference on Hybrid Artificial Intelligence Systems (HAIS2014)", doi = "10.1007/978-3-319-07617-1_29", isbn = "978-3-319-07616-4", issn = "0302-9743", keywords = "Time series segmentation, genetic algorithms, clustering, paleoclimate data, tipping points, abrupt climate change", month = "11th-13th September", organization = "Salamanca (Spain)", pages = "318--329", publisher = "Springer", series = "Lecture Notes in Computer Science", title = "{T}ime series segmentation of paleoclimate tipping points by an evolutionary algorithm", url = "http://dx.doi.org/10.1007/978-3-319-07617-1_29", volume = "8480", year = "2014", }

Abstract:

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.