Non-stationary Time Series Clustering with Application to Climate Systems

  • Mohammad Gorji Sefidmazgi
  • Mohammad Sayemuzzaman
  • Abdollah Homaifar
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 312)


In climate science, knowledge about the system mostly relies on measured time series. A common problem of highest interest is the analysis of high-dimensional time series having different phases. Clustering in a multi-dimensional non-stationary time series is challenging since the problem is ill- posed. In this paper, the Finite Element Method of non-stationary clustering is applied to find regimes and the long-term trends in a temperature time series. One of the important attributes of this method is that it does not depend on any statistical assumption and therefore local stationarity of time series is not necessary. Results represent low-frequency variability of temperature and spatiotemporal pattern of climate change in an area despite higher frequency harmonics in time series.


Non-stationary time series Time series clustering spatiotemporal pattern 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mohammad Gorji Sefidmazgi
    • 1
  • Mohammad Sayemuzzaman
    • 2
  • Abdollah Homaifar
    • 1
  1. 1.Department of Electrical EngineeringAutonomous Control and Information Technology CenterGreensboroUSA
  2. 2.Department of Environmental EngineeringNorth Carolina A&T State UniversityGreensboroUSA

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