Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices

Manel Hamdi (), Chaker Aloui and Santosh kumar Nanda ()
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Manel Hamdi: International Finance Group Tunisia, El Manar University, Tunisia
Santosh kumar Nanda: Eastern Academy of Science and Technology Prachivihar, Anantapu, Odisha, 754001, India

Economics Bulletin, 2016, vol. 36, issue 4, 2430-2442

Abstract: In this paper a trigonometric functional link artificial neural network (FLANN) model using backpropagation rule is applied to predict the next day's spot price of US crude oil. The daily observations of these variables: US dollar index, S&P 500 stock price index, gold spot price, heating oil spot price and US crude oil spot price are employed as inputs of the proposed model. By comparing with multilayer backpropagation feedforward neural network (FNN), more accurate predictions were shown by applying the FLANN model. In fact, several performance criteria are used to assess the forecasting power of the proposed model such as the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE) and the hit rate. For checking the forecasting robustness of the proposed model, in addition to the other input variables, the US crude oil and biofuels production are also used to predict the next month's spot price of crude oil. Comparatively, similar conclusion was deduced and the FLANN model performs better than the standard FNN. These findings can be explained by the simplicity of FLANN structure since it consists of a single layer with only one neuron at the output thus a lower computational load on the network.

Keywords: Crude oil price; Forecasting; Functional link artificial neural network (FLANN); Multilayer feedforward neural network (FNN). (search for similar items in EconPapers)
JEL-codes: C5 Q4 (search for similar items in EconPapers)
Date: 2016-12-10
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Citations: View citations in EconPapers (1)

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