World Library  

Add to Book Shelf
Flag as Inappropriate
Email this Book

Trend Analysis Using Non-stationary Time Series Clustering Based on the Finite Element Method : Volume 21, Issue 3 (23/05/2014)

By Gorji Sefidmazgi, M.

Click here to view

Book Id: WPLBN0003992625
Format Type: PDF Article :
File Size: Pages 11
Reproduction Date: 2015

Title: Trend Analysis Using Non-stationary Time Series Clustering Based on the Finite Element Method : Volume 21, Issue 3 (23/05/2014)  
Author: Gorji Sefidmazgi, M.
Volume: Vol. 21, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


APA MLA Chicago

Sefidmazgi, M. G., Homaifar, A., Liess, S., Jha, M. K., & Sayemuzzaman, M. (2014). Trend Analysis Using Non-stationary Time Series Clustering Based on the Finite Element Method : Volume 21, Issue 3 (23/05/2014). Retrieved from

Description: North Carolina A&T State University, Dept. of Electrical Engineering, Greensboro, USA. In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950–2009 can be explained mostly by AMO and solar activity.

Trend analysis using non-stationary time series clustering based on the finite element method

Aster, R. C. and Thurber, C. H.: Parameter estimation and inverse problems, Academic Press, Waltham, MA, 2012.; Beaulieu, C., Chen, J., and Sarmiento, J. L.: Change-point analysis as a tool to detect abrupt climate variations, Philos. Trans. R. Soc. Math. Phys. Eng. Sci., 370, 1228–1249, 2012a.; Beaulieu, C., Sarmiento, J. L., Mikaloff Fletcher, S. E., Chen, J., and Medvigy, D.: Identification and characterization of abrupt changes in the land uptake of carbon, Glob. Biogeochem. Cy., 26, GB1007, doi:10.1029/2010GB004024, 2012b.; Boyd, S. P. and Vandenberghe, L.: Convex optimization, Cambridge University Press, Cambridge, UK, 2004.; Boyles, R. P. and Raman, S.: Analysis of climate trends in North Carolina (1949–1998), Environ. Int., 29, 263–275, 2003.; Bililign, S., Lin, Y.-L., Davis, R., Ilias, S., Kurkalova, L., Kyei, Y., Rastigejev, Y., Uzochukwu, G., and Bae, S.: Effects of global warming on North Carolina, Int. J. Clim. Change Impacts Responses, 3, 53–70, 2012.; Conrad, V.: Methods in climatology, Harvard University Press, Cambridge, MA, 1944.; Della-Marta, P. M. and Wanner, H.: A method of homogenizing the extremes and mean of daily temperature measurements, J. Climate, 19, 4179–4197, 2006.; Dose, V. and Menzel, A.: Bayesian analysis of climate change impacts in phenology, Global Change Biol., 10, 259–272, 2004.; Ehsanzadeh, E., Ouarda, T. B. M. J., and Saley, H. M.: A simultaneous analysis of gradual and abrupt changes in Canadian low streamflows, Hydrol. Process, 25, 727–739, 2011.; Enfield, D. B., Mestas-Nuñez, A. M., and Trimble, P. J.: The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental US, Geophys. Res. Lett., 28, 2077–2080, 2001.; Frame, T. H. A. and Gray, L. J.: The 11-Yr solar cycle in ERA-40 data: An update to 2008, J. Climate, 23, 2213–2222, 2010.; Gallagher, C., Lund, R., and Robbins, M.: Changepoint detection in climate time series with long-term trends, J. Climate, 26, 4994–5006, 2013.; Godtliebsen, F., Holmström, L., Miettinen, A., Erästö, P., Divine, D. V., and Koc, N.: Pairwise scale space comparison of time series with application to climate research, J. Geophys. Res.-Ocean., 117, C03046, doi:10.1029/2011JC007546, 2012.; Gurobi Optimization: Gurobi optimizer reference manual, available at: (last access: 15 January 2014), 2014.; Hannart, A. and Naveau, P.: Bayesian multiple change points and segmentation: Application to homogenization of climatic series, Water Resour. Res., 45, W10444, doi:10.1029/2008WR007689, 2009.; Hathaway, D. H.: The solar cycle, Living Rev. Sol. Phys., 7, doi:10.12942/lrsp-2010-1, 2010.; Horenko, I.: Finite element approach to clustering of multidimensional time series, SIAM J. Sci. Comput., 32, 62–83, 2010a.; Horenko, I.: On clustering of non-stationary meteorological time series, Dynam. Atmos.-Ocean., 49, 164–187, 2010b.; Horenko, I.: On the Identification of Nonstationary Factor Models and Their Application to Atmospheric Data Analysis, J. Atmos. Sci., 67, 1559–1574, 2010c.; IPCC: Climate Change 2013 – The Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 2013.; Karl, T. R., Knight, R. W., and Baker, B.: The record breaking global temperatures of 1997 and 1998: Evidence for an increase in the rate of global warming?, Geophys. Res. Lett., 27, 719–722, 2000.; Kehagias, Ath. and Fortin, V.: Time series segmentation with shifting means hidden markov models, Nonlin. Processes Geophys., 13, 339–352, doi:10.5194/npg-13-339-2006, 2006.; Kiely, G.: Climate change in Ireland fro


Click To View

Additional Books

  • Turbulent Thermal Diffusion of Aerosols ... (by )
  • Assessment of Numerical Schemes for Solv... (by )
  • Tidally Induced Internal Motion in an Ar... (by )
  • 3D Reconnection Due to Oblique Modes: a ... (by )
  • Scale Free Properties in a Network-based... (by )
  • Finite-time Lagrangian Transport Analysi... (by )
  • Detecting Spatial Patterns with the Cumu... (by )
  • Distinguishing the Effects of Internal a... (by )
  • Obliquely Propagating Large Amplitude So... (by )
  • Analysis of Asymmetries in Propagating M... (by )
  • A Propagation-separation Approach to Est... (by )
  • Thin and Superthin Ion Current Sheets. Q... (by )
Scroll Left
Scroll Right


Copyright © World Library Foundation. All rights reserved. eBooks from Hawaii eBook Library are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.