• 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 
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.


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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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