Forecasting with computer-evolved model specifications: a genetic programming application

This paper uses genetic programming (GP) to evolve model specifications of time series data. GP is a computerized random search optimization algorithm that assembles equations until it identifies the fittest one. The technique is applied here to artificially simulated data first then to real-world sunspot numbers. One-step-ahead forecasts produced by the fittest of computer-evolved models are evaluated and compared with alternatives. The results suggest that GP may produce reasonable forecasts if their user selects appropriate input variables and comprehends the process investigated. Further, the technique appears promising in forecasting noisy complex series perhaps better than other existing methods. It is suitable for decision makers who set high priority on obtaining accurate forecasts rather than on probing into and approximating the underlying data generating process.