Abstract
Forecasting oil prices remains an important empirical issue. This paper compares three forecasts of short-term oil prices using two compumetric methods and naive random walk. Compumetric methods use model specifications generated by computers with limited human intervention. Users are responsible only for selecting the appropriate set of explanatory variables. The compumetric methods employed here are genetic programming and artificial neural networks. The variable to forecast is monthly US imports FOB oil prices. Each method is used to forecast one and three months ahead. The results suggest that neural networks deliver better predictions.
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