elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

On the use of evolutionary time series analysis for segmenting paleoclimate data.

Perez-Ortiz, M. and Duran-Rosal, A. and Gutierrez, P.J. and Sanchez-Monedero, J. and Nikolaou, Athanasia and Fernandez-Navarro, F. and Hervas-Martinez, C. (2017) On the use of evolutionary time series analysis for segmenting paleoclimate data. Neurocomputing. Elsevier. DOI: 10.1016/j.neucom.2016.11.101 ISSN 0925-2312

Full text not available from this repository.

Official URL: http://www.sciencedirect.com/science/article/pii/S0925231217315345

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.

Item URL in elib:https://elib.dlr.de/114768/
Document Type:Article
Additional Information:Bisher nur online erschienen.
Title:On the use of evolutionary time series analysis for segmenting paleoclimate data.
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Perez-Ortiz, M.Department of Quantitative Methods, Universidad Loyola Andalucía, Third Building, Córdoba 14004, SpainUNSPECIFIED
Duran-Rosal, A.Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein building, Córdoba 14071, SpainUNSPECIFIED
Gutierrez, P.J.iaa-csic, granada, spainUNSPECIFIED
Sanchez-Monedero, J.Department of Quantitative Methods, Universidad Loyola Andalucía, Third Building, Córdoba 14004, SpainUNSPECIFIED
Nikolaou, AthanasiaAthanasia.Nikolaou (at) dlr.deUNSPECIFIED
Fernandez-Navarro, F.Department of Quantitative Methods, Universidad Loyola Andalucía, Third Building, Córdoba 14004, SpainUNSPECIFIED
Hervas-Martinez, C.Department of Computer Science and Numerical Analysis, University of Córdoba, Rabanales Campus, Albert Einstein building, Córdoba 14071, SpainUNSPECIFIED
Date:September 2017
Journal or Publication Title:Neurocomputing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1016/j.neucom.2016.11.101
Publisher:Elsevier
ISSN:0925-2312
Status:Published
Keywords:Time series segmentation, Genetic algorithms, Clustering, Paleoclimate data, Tipping points, Abrupt climate change,
HGF - Research field:other
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:no assignment
DLR - Program:no assignment
DLR - Research theme (Project):no assignment, R - Vorhaben Exploration des Sonnensystems
Location: Berlin-Adlershof
Institutes and Institutions:Institute of Planetary Research > Planetary Physics
Deposited By: Nikolaou, Athanasia
Deposited On:10 Nov 2017 12:42
Last Modified:06 Sep 2019 15:18

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.