IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1296-d1353373.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1296/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/6/1296/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Lean & Ma, Yueming & Ma, Mengyao, 2021. "An effective rolling decomposition-ensemble model for gasoline consumption forecasting," Energy, Elsevier, vol. 222(C).
    2. Drachal, Krzysztof, 2021. "Forecasting selected energy commodities prices with Bayesian dynamic finite mixtures," Energy Economics, Elsevier, vol. 99(C).
    3. Cindy W. Ma, 1989. "Forecasting efficiency of energy futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 9(5), pages 393-419, October.
    4. M. E. Malliaris & S. G. Malliaris, 2008. "Forecasting inter-related energy product prices," The European Journal of Finance, Taylor & Francis Journals, vol. 14(6), pages 453-468.
    5. Gogas, Periklis & Papadimitriou, Theophilos & Sofianos, Emmanouil, 2019. "Money Neutrality, Monetary Aggregates and Machine Learning," DUTH Research Papers in Economics 4-2016, Democritus University of Thrace, Department of Economics.
    6. Periklis Gogas & Theophilos Papadimitriou, 2021. "Machine Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 1-4, January.
    7. Li, Ranran, 2023. "Forecasting energy spot prices: A multiscale clustering recognition approach," Resources Policy, Elsevier, vol. 81(C).
    8. Shian-Chang Huang & Cheng-Feng Wu, 2018. "Energy Commodity Price Forecasting with Deep Multiple Kernel Learning," Energies, MDPI, vol. 11(11), pages 1-16, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Raushan Kumar, 2021. "Predicting Wheat Futures Prices in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(1), pages 121-140, March.
    2. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    3. Afaq Khattak & Hamad Almujibah & Ahmed Elamary & Caroline Mongina Matara, 2022. "Interpretable Dynamic Ensemble Selection Approach for the Prediction of Road Traffic Injury Severity: A Case Study of Pakistan’s National Highway N-5," Sustainability, MDPI, vol. 14(19), pages 1-18, September.
    4. Li, He & Fang, Debin & Zhao, Chaoyang, 2024. "Retail competition among multi-type retail electric providers in social networks," Energy Economics, Elsevier, vol. 132(C).
    5. Adil EL Fakir & Richard Fairchild & Youssef Lamrani Alaoui & Dora Chan & Mohamed Tkiouat & Zaid Amer, 2024. "Kinship, gender and social links impact on micro group lending defaults," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2527-2542, April.
    6. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    7. Peter Dixon & Michael Jerie & Dean Mustakinov & Maureen T. Rimmer & Nicholas Sheard & Florian Schiffmann & Glyn Wittwer, 2024. "Constructing a Destructive Events Tool using Small Rectangular Areas, Computable General Equilibrium Modelling and Neural Networks," Centre of Policy Studies/IMPACT Centre Working Papers g-349, Victoria University, Centre of Policy Studies/IMPACT Centre.
    8. Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    9. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
    10. Heyam H. Al-Baity, 2023. "The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    11. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
    12. Nwafor, Chioma Ngozi & Nwafor, Obumneme Zimuzor, 2023. "Determinants of non-performing loans: An explainable ensemble and deep neural network approach," Finance Research Letters, Elsevier, vol. 56(C).
    13. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).
    14. Michael Ye & John Zyren & Joanne Shore & Thomas Lee, 2010. "Crude Oil Futures as an Indicator of Market Changes: A Graphical Analysis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 16(3), pages 257-268, August.
    15. Oscar Varela, 1999. "Futures and realized cash or settle prices for gold, silver, and copper," Review of Financial Economics, John Wiley & Sons, vol. 8(2), pages 121-138, September.
    16. Xiangjun Cai & Dagang Li & Li Feng, 2024. "Enhanced Carbon Price Forecasting Using Extended Sliding Window Decomposition with LSTM and SVR," Mathematics, MDPI, vol. 12(23), pages 1-20, November.
    17. Ma, Yixiang & Yu, Lean & Zhang, Guoxing, 2022. "Short-term wind power forecasting with an intermittency-trait-driven methodology," Renewable Energy, Elsevier, vol. 198(C), pages 872-883.
    18. Bilgin, Rumeysa, 2023. "The Selection Of Control Variables In Capital Structure Research With Machine Learning," SocArXiv e26qf, Center for Open Science.
    19. Hankyeung Choi & David J. Leatham & Kunlapath Sukcharoen, 2015. "Oil Price Forecasting Using Crack Spread Futures and Oil Exchange Traded Funds," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 9(1), March.
    20. Melike Bildirici & Nilgun Guler Bayazit & Yasemen Ucan, 2020. "Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM," Energies, MDPI, vol. 13(11), pages 1-18, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1296-:d:1353373. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.