• James B. Elsner
  • Anastasios A. Tsonis


We have shown how SSA can be used to filter a time series to retain desired modes of variability and further how to use SSA to extract a nonlinear trend. Here we discuss how the predictability of a system can be improved by forecasting the important oscillations in a time series taken from the system. The general idea is to filter the record first and then use some time-series model to forecast on the filtered series. There are a couple of time-series models for prediction to choose from. We first present the overall prediction strategy with reference to an autoregressive (AR) model. Then we demonstrate a prediction algorithm that does not require an underlying model.


Lead Time Southern Oscillation Index Prediction Strategy Singular Spectrum Analysis Reconstructed Component 


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Copyright information

© Springer Science+Business Media New York 1996

Authors and Affiliations

  • James B. Elsner
    • 1
  • Anastasios A. Tsonis
    • 2
  1. 1.Florida State UniversityTallahasseeUSA
  2. 2.University of Wisconsin-MilwaukeeMilwaukeeUSA

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