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Long-term forecast of energy commodities price using machine learning

Gabriel Paes Herrera, Michel Constantino, Benjamin Tabak (), Hemerson Pistori, Jen-Je Su and Athula Naranpanawa

Energy, 2019, vol. 179, issue C, 214-221

Abstract: We compare the long-horizon forecast performance of traditional econometric models with machine learning methods (Neural Networks and Random Forests) for the main energy commodities in the world using monthly prices provided by the International Monetary Fund (IMF). We study the case of Oil (Brent, WTI and Dubai Fateh), Coal (AU) and Gas (US and Russia). Models accuracy are measured using RMSE and MAPE and the M-DM test is applied to evaluate whether there is a statistically significant difference between the methods. We computed thousands of tests regarding the machine learning parameters combinations as there is no method to set the optimal structure for these models. The results show that machine learning methods outperform traditional econometric methods and also that they present an additional advantage, which is the capacity to predict turning points. This study adds further evidence for the discussion on the use of machine learning algorithms for the development of more accurate forecasts to support policymakers and help the decision-making process in the international energy market.

Keywords: ANN; Random forests; Natural gas; Coal; Oil (search for similar items in EconPapers)
Date: 2019
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Handle: RePEc:eee:energy:v:179:y:2019:i:c:p:214-221