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Volume 17
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Article

Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms

by
Emmanouil Sofianos
1,
Emmanouil Zaganidis
2,
Theophilos Papadimitriou
2 and
Periklis Gogas
2,*
1
Bureau d’Economie Théorique et Appliquée (BETA), University of Strasbourg, 67085 Strasbourg, France
2
Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece
*
Author to whom correspondence should be addressed.
Energies 2024, 17(6), 1296; https://doi.org/10.3390/en17061296
Submission received: 29 January 2024 / Revised: 29 February 2024 / Accepted: 4 March 2024 / Published: 8 March 2024
(This article belongs to the Special Issue Emerging Trends in Energy Economics II)
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Abstract

This study aims to forecast New York and Los Angeles gasoline spot prices on a daily frequency. The dataset includes gasoline prices and a big set of 128 other relevant variables spanning the period from 17 February 2004 to 26 March 2022. These variables were fed to three tree-based machine learning algorithms: decision trees, random forest, and XGBoost. Furthermore, a variable importance measure (VIM) technique was applied to identify and rank the most important explanatory variables. The optimal model, a trained random forest, achieves a mean absolute percent error (MAPE) in the out-of-sample of 3.23% for the New York and 3.78% for the Los Angeles gasoline spot prices. The first lag, AR (1), of gasoline is the most important variable in both markets; the top five variables are all energy-related. This paper can strengthen the understanding of price determinants and has the potential to inform strategic decisions and policy directions within the energy sector, making it a valuable asset for both industry practitioners and policymakers.
Keywords: gasoline; decision tree; random forest; XGBoost; machine learning; forecasting gasoline; decision tree; random forest; XGBoost; machine learning; forecasting

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MDPI and ACS Style

Sofianos, E.; Zaganidis, E.; Papadimitriou, T.; Gogas, P. Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms. Energies 2024, 17, 1296. https://doi.org/10.3390/en17061296

AMA Style

Sofianos E, Zaganidis E, Papadimitriou T, Gogas P. Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms. Energies. 2024; 17(6):1296. https://doi.org/10.3390/en17061296

Chicago/Turabian Style

Sofianos, Emmanouil, Emmanouil Zaganidis, Theophilos Papadimitriou, and Periklis Gogas. 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms" Energies 17, no. 6: 1296. https://doi.org/10.3390/en17061296

APA Style

Sofianos, E., Zaganidis, E., Papadimitriou, T., & Gogas, P. (2024). Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms. Energies, 17(6), 1296. https://doi.org/10.3390/en17061296

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