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Forecasting Spanish Olive Oil Prices Using Machine Learning: A Comprehensive Analysis of Contributing Factors and Model Performance

Mohamed Rochdi Keffala & Safoua Mahjoub

Journal of Agricultural & Food Industrial Organization2025https://doi.org/10.1515/jafio-2024-0067article
AJG 1ABDC C
Weight
0.50

What the paper says

Abstract The main goal of this study was to create a strong predictive model for forecasting olive oil prices. To do this, we applied four machine learning models in Python: Random Forest, Gradient Boosting, Decision Tree, and Support Vector Regression, using 164 monthly price observations along with factors like temperature, precipitation, consumer price index, IBEX35 stock market prices, EUR/USD exchange rate, and import/export quantities. The results showed that Random Forest and Gradient Boosting models performed the best. The Spearman correlation analysis revealed that the exchange rate had a strong negative correlation with prices, while the consumer price index and import quantity had moderate positive correlations. Random Forest highlighted the consumer price index as the most important factor in predicting olive oil prices. This study fills a gap in existing research and provides practical insights for companies in the olive oil industry to better monitor and forecast prices, helping with profitability, risk management, stock optimization, and investment decisions.

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https://doi.org/https://doi.org/10.1515/jafio-2024-0067

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@article{mohamed2025,
  title        = {{Forecasting Spanish Olive Oil Prices Using Machine Learning: A Comprehensive Analysis of Contributing Factors and Model Performance}},
  author       = {Mohamed Rochdi Keffala & Safoua Mahjoub},
  journal      = {Journal of Agricultural & Food Industrial Organization},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1515/jafio-2024-0067},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.