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

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• 作者：Francesco Finazzi (1)
  Ruth Haggarty (2)
  Claire Miller (2)
  Marian Scott (2)
  Alessandro Fass貌 (3)
  
  1. Department of Management ; Economics and Quantitative Methods ; University of Bergamo ; via dei Caniana 2 ; 24127聽 ; Bergamo ; Italy
  2. School of Mathematics and Statistics ; University of Glasgow ; 15 University Gardens ; Glasgow聽 ; G12 8QW ; UK
  3. Department of Engineering ; University of Bergamo ; viale Marconi 5 ; 24044聽 ; Dalmine ; Italy
  
• 关键词：State space ; Expectation maximization ; Functional data analysis ; Splines
• 刊名：Stochastic Environmental Research and Risk Assessment (SERRA)
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：29
• 期：2
• 页码：463-475
• 全文大小：2,272 KB
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• 刊物类别：Earth and Environmental Science
• 刊物主题：Environment
  Mathematical Applications in Environmental Science
  Mathematical Applications in Geosciences
  Probability Theory and Stochastic Processes
  Statistics for Engineering, Physics, Computer Science, Chemistry and Geosciences
  Numerical and Computational Methods in Engineering
  Waste Water Technology, Water Pollution Control, Water Management and Aquatic Pollution
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1436-3259

文摘

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.