Summary
In this paper a system based on Genetic Programming for forecasting nonlinear time series is outlined. Our system is endowed with two features. Firstly, at any given time t, it performs a τ-steps ahead prediction (i.e. it forecasts the value at time t + τ) based on the set of input values for the n time steps preceding t. Secondly, the system automatically finds among the past n input variables the most useful ones to estimate future values. The effectiveness of our approach is evaluated on El Niño 3.4 time series on the basis of a 12-month-ahead forecast.
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De Falco, I., Della Cioppa, A., Tarantino, E. (2005). A Genetic Programming System for Time Series Prediction and Its Application to El Niño Forecast. In: Hoffmann, F., Köppen, M., Klawonn, F., Roy, R. (eds) Soft Computing: Methodologies and Applications. Advances in Soft Computing, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32400-3_12
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DOI: https://doi.org/10.1007/3-540-32400-3_12
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