Change Detection in Climate Time Series Based on Bounded-Variation Clustering
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.
KeywordsTime series Non-stationary Change detection Abrupt climate change Autocorrelation
- Gorji Sefidmazgi M, Moradi Kordmahalleh M, Homaifar A, Karimoddini A (2014a) A finite element based method for identification of switched linear systems. In: American Control Conference (ACC). IEEE, Portland, USA, pp 2644–2649. doi:10.1109/ACC.2014.6858898
- Gorji Sefidmazgi M, Sayemuzzaman M, Homaifar A (2014b) Non-stationary time series clustering with application to climate systems. In: Jamshidi M, Kreinovich V, Kacprzyk J (eds) Advance trends in soft computing, vol 312. Studies in fuzziness and soft computing. Springer International Publishing, Switzerland, pp 55–63. doi:10.1007/978-3-319-03674-8_6
- Gurobi (2014) Gurobi optimizer reference manual, Houston, USAGoogle Scholar
- Horenko I (2010a) On clustering of non-stationary meteorological time series. Dyn Atmos Ocean 49(2–3):164–187. doi:http://dx.doi.org/10.1016/j.dynatmoce.2009.04.003
- Liu RQ, Jacobi C, Hoffmann P, Stober G, Merzlyakov EG (2010) A piecewise linear model for detecting climatic trends and their structural changes with application to mesosphere/lower thermosphere winds over Collm, Germany. J Geophys Res Atmos 115(D22), D22105. doi:10.1029/2010JD014080 CrossRefGoogle Scholar