Change Detection in Climate Time Series Based on Bounded-Variation Clustering

  • Mohammad Gorji Sefidmazgi
  • Mina Moradi Kordmahalleh
  • Abdollah Homaifar
  • Stefan Liess


Climate time series are generally nonstationary which means that their statistical properties change with time. Analysis of nonstationary time series requires detecting of change points between a set of clusters, where model of time series in each cluster has different statistical parameters. Common change detection methods are based on assumptions that may not be valid generally. Bounded-variation clustering can solve the change detection problem with minimum restrictive assumptions. In this paper, this method is employed to detect the pattern of changes in the Pacific Decadal Oscillation and the piecewise linear trend of US temperature. An optimal number of the change points are found with the Bayesian information criterion.


Time series Non-stationary Change detection Abrupt climate change Autocorrelation 



This work is supported by the Expeditions in Computing by the National Science Foundation under Award Number: CCF-1029731: Expedition in Computing: Understanding Climate Change.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mohammad Gorji Sefidmazgi
    • 1
  • Mina Moradi Kordmahalleh
    • 1
  • Abdollah Homaifar
    • 2
  • Stefan Liess
    • 3
  1. 1.Department of Electrical EngineeringNorth Carolina A&T State UniversityGreensboroUSA
  2. 2.Department of Electrical and Computer EngineeringNorth Carolina Agricultural and Technical State UniversityGreensboroUSA
  3. 3.Department of Soil, Water, and ClimateUniversity of MinnesotaMinneapolisUSA

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