Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms.
In: Energies (19961073), Jg. 17 (2024-03-15), Heft 6, S. 1296-1309
Online
academicJournal
Zugriff:
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. [ABSTRACT FROM AUTHOR]
Titel: |
Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms.
|
---|---|
Autor/in / Beteiligte Person: | Sofianos, Emmanouil ; Zaganidis, Emmanouil ; Papadimitriou, Theophilos ; Gogas, Periklis |
Link: | |
Zeitschrift: | Energies (19961073), Jg. 17 (2024-03-15), Heft 6, S. 1296-1309 |
Veröffentlichung: | 2024 |
Medientyp: | academicJournal |
ISSN: | 1996-1073 (print) |
DOI: | 10.3390/en17061296 |
Schlagwort: |
|
Sonstiges: |
|