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

Author

Listed:
  • Emmanouil Sofianos

    (Bureau d’Economie Théorique et Appliquée (BETA), University of Strasbourg, 67085 Strasbourg, France)

  • Emmanouil Zaganidis

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Theophilos Papadimitriou

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

  • Periklis Gogas

    (Department of Economics, Democritus University of Thrace, 69100 Komotini, Greece)

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.

Suggested Citation

  • Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1296-:d:1353373
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    References listed on IDEAS

    as
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