A genetic programming system for time series prediction and its application to el niño forecast

Abstract

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.