Forecasting economic time series with the DyFor genetic program model

Abstract

Genetic programming (GP) uses the Darwinian principle of survival of the fittest and sexual recombination to evolve computer programs that solve problems. Several studies have applied GP to forecasting with favourable results. However, these studies, like others, have assumed a static environment, making them unsuitable for many real-world time series which are generated by varying processes. This study investigates the development of a new 'dynamic' GP model that is specifically tailored for forecasting in nonstatic environments. This dynamic forecasting genetic program (DyFor GP) model incorporates methods to adapt to changing environments automatically as well as retain knowledge learned from previously encountered environments. The DyFor GP model is tested on real-world economic time series, namely the US Gross Domestic Product and Consumer Price Index Inflation. Results show that the DyFor GP model outperforms benchmark models from leading studies for both experiments. These findings affirm the DyFor GP's potential as an adaptive, nonlinear forecasting model.