Abstract
In this paper, a hybrid system combining neural networks and genetic training is designed to forecast future oil prices. The architectural design is that of the multilayer back propagation network that is fed monthly prices for West Texas Intermediate covering the period 1986–2014. The model’s predictions are compared to those of the one, two, three, and four-month futures prices and are evaluated both on their level of accuracy as well as correctness. While accuracy measures the degree of error, correctness tests the model’s ability to predict the direction of the movement. By processing information more efficiently, and identifying patterns that may be ill-defined as a result of pronounced price volatility, this paper aims to improve the accuracy of oil price forecasts.
Published Version
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