Theoretical and Applied Climatology

, Volume 120, Issue 3–4, pp 433–444 | Cite as

Prediction of periodically correlated processes by wavelet transform and multivariate methods with applications to climatological data

  • Mitra Ghanbarzadeh
  • Mina AminghafariEmail author
Original Paper


This article studies the prediction of periodically correlated process using wavelet transform and multivariate methods with applications to climatological data. Periodically correlated processes can be reformulated as multivariate stationary processes. Considering this fact, two new prediction methods are proposed. In the first method, we use stepwise regression between the principal components of the multivariate stationary process and past wavelet coefficients of the process to get a prediction. In the second method, we propose its multivariate version without principal component analysis a priori. Also, we study a generalization of the prediction methods dealing with a deterministic trend using exponential smoothing. Finally, we illustrate the performance of the proposed methods on simulated and real climatological data (ozone amounts, flows of a river, solar radiation, and sea levels) compared with the multivariate autoregressive model. The proposed methods give good results as we expected.


Discrete Wavelet Transform Wavelet Coefficient Continuous Wavelet Transform Exponential Smoothing Climatological Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We would like thanks the editor and the anonymous referee for careful reading and constructive suggestions on the structure of the manuscript. The authors are also grateful to Professors Jean-Michel Poggi for his valuable comments and precise suggestions.


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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  1. 1.Department of Statistics, Faculty of Mathematics and Computer ScienceAmirkabir University of TechnologyTehranIran

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