A comparison of clustering approaches for the study of the temporal coherence of multiple time series

Finazzi, F., Haggarty, R., Miller, C. , Scott, M. and Fassò, A. (2015) A comparison of clustering approaches for the study of the temporal coherence of multiple time series. Stochastic Environmental Research and Risk Assessment, 29(2), pp. 463-475. (doi:10.1007/s00477-014-0931-2)

[img]
Preview
Text
96715.pdf - Published Version
Available under License Creative Commons Attribution.

2MB
[img]
Preview
Text
96715(1).pdf

61kB

Abstract

Two approaches for clustering of time series have been considered. The first is a novel approach based on a modification of classic state-space modelling while the second is based on functional clustering. For the latter, both k-means and complete-linkage hierarchical clustering algorithms are adopted. The two approaches are compared using a simulation study, and are applied to lake surface water temperature for 256 lakes globally for 5 years of data, to investigate information obtained from each approach.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:O'Donnell, Dr Ruth and Finazzi, Dr Francesco and Miller, Dr Claire
Authors: Finazzi, F., Haggarty, R., Miller, C., Scott, M., and Fassò, A.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Stochastic Environmental Research and Risk Assessment
Publisher:Springer Verlag
ISSN:1436-3240
ISSN (Online):1436-3259
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Stochastic Environmental Research and Risk Assessment 29(2):463-475
Publisher Policy:Reproduced under a Creative Commons License

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
595861Global Observatory of Lake Responses to Environmental Change (GloboLakes).Claire MillerNatural Environment Research Council (NERC)NE/J022810/1M&S - STATISTICS