Prediction OPEC oil price utilizing long short-term memory and multi-layer perceptron models - Hiyam Abdulrahim; Safiya Mukhtar Alshibani; Omer Ibrahim; Azhari A. Elhag (Alexandria Engineering Journal)
The present study undertakes a comprehensive assessment of two predictive models, namely Long Short-Term Memory (LSTM) and Multi-layer Perceptron (MLP), with a specific emphasis on their effectiveness in predicting oil prices, particularly those of the Petroleum Exporting Countries (OPEC). In this study, three fundamental statistical measures are utilized: The Symmetric Mean Absolute Percentage Error (SMAPE), the Mean Squared Error (MSE), and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the LSTM model regularly surpasses the MLP model in the three benchmarks. In particular, the LSTM model demonstrates lower values for SMAPE, MSE, and MAPE, indicating higher prediction accuracy. The decreased error scores linked to the LSTM model highlight its improved capacity for precise oil price prediction in comparison to the MLP model. These results signify a notable progress in the use of machine learning techniques for predicting OPEC oil prices. Moreover, this study provides invaluable perspectives for OPEC management, policymakers, and organizations focused on oil price fluctuations, therefore contributing to the wider endeavour of enhancing the stability and economic sustainability of the oil pricing system in OPEC countries. The consequences of the study include the promotion of a pricing system that facilitates the achievement of economic and social development goals in these countries.