This paper applies the novel adaptive learning methodology to forecast agricultural and energy prices in Greece’s volatile, data-scarce markets. We combine traditional ordinary least squares with quantile regression techniques within this framework, achieving up to 27% lower forecast errors compared to conventional benchmarks. Our analysis reveals distinct performance patterns: quantile regression demonstrates superior accuracy for volatile commodities (e.g., barley), while ordinary least squares performs better for stable markets (e.g., maize). The learning rate parameter γ proves crucial in adapting to market conditions. These findings provide policymakers with an enhanced tool for analyzing energy-agriculture price linkages and managing market volatility, particularly in small, open economies facing data limitations.
References
[1]
Allen, P. G. (1994). A Note on Forecasting with Econometric Models. Northeastern Journal of Agricultural and Resource Economics, 13, 264-267. https://doi.org/10.1017/s0899367x00000593
[2]
Baimpos, G., & Kyriazi, F. (2025). Nonlinear GDP Forecasting: A Threshold-ARDLX Approach with Leading Macroeconomic Indicators. Theoretical Economics Letters, 15, 446-460. https://doi.org/10.4236/tel.2025.152024
[3]
Bastian, J., Zhu, J. X., Banunarayanan, V., & Mukerji, R. (1999). Forecasting Energy Prices in a Competitive Market. IEEE Computer Applications in Power, 12, 40-45. https://doi.org/10.1109/67.773811
[4]
Ben Ameur, H., Boubaker, S., Ftiti, Z., Louhichi, W., & Tissaoui, K. (2024). Forecasting Commodity Prices: Empirical Evidence Using Deep Learning Tools. Annals of Operations Research, 339, 349-367. https://doi.org/10.1007/s10479-022-05076-6
[5]
Ben-Ari, T., Adrian, J., Klein, T., Calanca, P., Van der Velde, M., & Makowski, D. (2016). Identifying Indicators for Extreme Wheat and Maize Yield Losses. Agricultural and Forest Meteorology, 220, 130-140. https://doi.org/10.1016/j.agrformet.2016.01.009
[6]
Chou, J., & Tran, D. (2018). Forecasting Energy Consumption Time Series Using Machine Learning Techniques Based on Usage Patterns of Residential Householders. Energy, 165, 709-726. https://doi.org/10.1016/j.energy.2018.09.144
[7]
Cleveland, W. S. (1971). Stochastic Modeling of Economic Time Series. Journal of the American Statistical Association, 66, 552-564.
[8]
Divina, F., García Torres, M., Goméz Vela, F. A., & Vázquez Noguera, J. L. (2019). A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings. Energies, 12, Article No. 1934. https://doi.org/10.3390/en12101934
[9]
Farooq, S. (2015). Forecasting Area and Production of Barley in Punjab. Pakistan Journal of Agricultural Research, 28, 304-309.
[10]
Ferrari, D., Ravazzolo, F., & Vespignani, J. (2021). Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach. Energy Economics, 98, Article ID: 105268. https://doi.org/10.1016/j.eneco.2021.105268
[11]
Fowowe, B. (2016). Do Oil Prices Drive Agricultural Commodity Prices? Evidence from South Africa. Energy, 104, 149-157. https://doi.org/10.1016/j.energy.2016.03.101
[12]
Gargano, A., & Timmermann, A. (2014a). Forecasting Commodity Prices. Journal of Fore-casting, 33, 405-419.
[13]
Gargano, A., & Timmermann, A. (2014b). Forecasting Commodity Price Indexes Using Macroeconomic and Financial Predictors. International Journal of Forecasting, 30, 825-843. https://doi.org/10.1016/j.ijforecast.2013.09.003
[14]
Groen, J. J. J., & Pesenti, P. A. (2011). Commodity Prices, Commodity Currencies, and Global Economic Developments. In T. Ito, & A. K. Rose (Eds.), Commodity Prices and Markets (pp. 15-42). University of Chicago Press. https://doi.org/10.7208/chicago/9780226386904.003.0002
[15]
Guerard, J. B., Thomakos, D., Kyriazi, F., & Beheshti, B. (2024). The Development and Evolution of Mean-Variance Efficient Portfolios in the US and Japan: 30 Years after the Markowitz and Ziemba Applications. Annals of Operations Research. https://doi.org/10.1007/s10479-024-06138-7
[16]
Han, L., Zhou, Y., & Yin, L. (2015). Exogenous Impacts on the Links between Energy and Agricultural Commodity Markets. Energy Economics, 49, 350-358. https://doi.org/10.1016/j.eneco.2015.02.021
[17]
Heij, C., van Dijk, D., & Groenen, P. J. F. (2011). Real-Time Macroeconomic Forecasting with Leading Indicators: An Empirical Comparison. International Journal of Forecasting, 27, 466-481. https://doi.org/10.1016/j.ijforecast.2010.04.008
[18]
Herrera, G. P., Constantino, M., Tabak, B. M., Pistori, H., Su, J., & Naranpanawa, A. (2019). Long-Term Forecast of Energy Commodities Price Using Machine Learning. Energy, 179, 214-221. https://doi.org/10.1016/j.energy.2019.04.077
[19]
Hong, T., Pinson, P., Wang, Y., Weron, R., Yang, D., & Zareipour, H. (2020). Energy Forecasting: A Review and Outlook. IEEE Open Access Journal of Power and Energy, 7, 376-388. https://doi.org/10.1109/oajpe.2020.3029979
[20]
Kehagia, A., & Kyriazi, F. (2021). Structural Funds and Regional Economic Growth: The Greek Experience. Review of Economic Analysis, 13, 501-532. https://doi.org/10.15353/rea.v13i3.3481
[21]
Koenker, R., & Bassett, G. (1978). Regression Quantiles. Econometrica, 46, 33-50. https://doi.org/10.2307/1913643
[22]
Kofi, T. A. (1973). A Framework for Comparing the Efficiency of Futures Markets. American Journal of Agricultural Economics, 55, 584-594. https://doi.org/10.2307/1238343
[23]
Kyriazi, F. (2024). The Prescriptive Nature of Market Timing and Predictive Portfolios. IMA Journal of Management Mathematics, 36, 323-338. https://doi.org/10.1093/imaman/dpae027
[24]
Kyriazi, F., & Thomakos, D. D. (2020a). Foreign Exchange Rate Predictability: Seek and Ye Shall Find It. In J. B. Guerard, & W. T. Ziemba (Eds.), Handbook of Applied Investment Research (pp. 511-556). World Scientific. https://doi.org/10.1142/9789811222634_0020
[25]
Kyriazi, F., & Thomakos, D. D. (2020b). Distance-Based Nearest Neighbour Forecasting with Application to Exchange Rate Predictability. IMA Journal of Management Mathematics, 31, 469-490. https://doi.org/10.1093/imaman/dpz016
[26]
Kyriazi, F., Thomakos, D. D., & Guerard, J. B. (2019). Adaptive Learning Forecasting, with Applications in Forecasting Agricultural Prices. International Journal of Forecasting, 35, 1356-1369. https://doi.org/10.1016/j.ijforecast.2019.03.031
[27]
Kyriazi, F., Xylangouras, E., & Papadogonas, T. (2024). On the Forecastability of Agricultural Output. Review of Economic Analysis, 16, 443-467. https://doi.org/10.15353/rea.v16i4.5620
[28]
Lehmann, E. L., & Romano, J. P. (2020). Testing Statistical Hypotheses (4th ed.). Spring-er.
[29]
Lekana, H. C., & Ikiemi, C. B. S. (2021). Effect of Energy Consumption on Human Development in the Countries of the Economic and Monetary Community of Central Africa (EMCCA). Theoretical Economics Letters, 11, 404-421. https://doi.org/10.4236/tel.2021.113027
[30]
López Cabrera, B., & Schulz, F. (2016). Volatility Linkages between Energy and Agricultural Commodity Prices. Energy Economics, 54, 190-203. https://doi.org/10.1016/j.eneco.2015.11.018
[31]
Manogna, R. L., & Mishra, A. K. (2021). Forecasting Spot Prices of Agricultural Commodities in India: Application of Deep‐Learning Models. Intelligent Systems in Accounting, Finance and Management, 28, 72-83. https://doi.org/10.1002/isaf.1487
[32]
Moore, H. L. (1917). Forecasting the Yield and Price of Cotton. Journal of Political Economy, 25, 1-24.
[33]
Naylor, T. H., Seaks, T. G., & Wichern, D. W. (1972). Box-Jenkins Methods: An Alternative to Econometric Models. International Statistical Review, 40, 123-137. https://doi.org/10.2307/1402755
[34]
Nikolopoulos, K. I., & Thomakos, D. D. (2019). Forecasting Analytics. In B. Pochiraju, & S. Seshadri (Eds.), Essentials of Business Analytics (pp. 381-420). Springer International Publishing. https://doi.org/10.1007/978-3-319-68837-4_12
[35]
Ouyang, H., Wei, X., & Wu, Q. (2019). Agricultural Commodity Futures Prices Prediction via Long-and Short-Term Time Series Network. Journal of Applied Economics, 22, 468-483. https://doi.org/10.1080/15140326.2019.1668664
[36]
Paris, A. (2018). On the Link between Oil and Agricultural Commodity Prices: Do Biofuels Matter? International Economics, 155, 48-60. https://doi.org/10.1016/j.inteco.2017.12.003
[37]
Parviz, L. (2018). Assessing Accuracy of Barley Yield Forecasting with Integration of Climate Variables and Support Vector Regression. Annales Universitatis Mariae Curie-Sklodowska, Sectio C—Biologia, 73, 19. https://doi.org/10.17951/c.2018.73.1.19-30
[38]
Pierce, D. A. (1977). Relationships—and the Lack Thereof—between Economic Time Series, with Special Reference to Money and Interest Rates. Journal of the American Statistical Association, 72, 11-22. https://doi.org/10.1080/01621459.1977.10479899
[39]
Qader, S. H., Dash, J., & Atkinson, P. M. (2018). Forecasting Wheat and Barley Crop Production in Arid and Semi-Arid Regions Using Remotely Sensed Primary Productivity and Crop Phenology: A Case Study in Iraq. Science of the Total Environment, 613, 250-262. https://doi.org/10.1016/j.scitotenv.2017.09.057
[40]
Reboredo, J. C. (2015). Is There Dependence and Systemic Risk between Oil and Renewable Energy Stock Prices? Energy Economics, 48, 32-45. https://doi.org/10.1016/j.eneco.2014.12.009
[41]
Rostan, P., & Rostan, A. (2021). Where Are Fossil Fuels Prices Heading? International Journal of Energy Sector Management, 15, 309-327. https://doi.org/10.1108/ijesm-07-2019-0009
[42]
Schachinger, D., Pannosch, J., & Kastner, W. (2018). Adaptive Learning-Based Time Series Prediction Framework for Building Energy Management. In 2018 IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES) (pp. 453-458). IEEE. https://doi.org/10.1109/ieses.2018.8349919
[43]
Serra, T., & Gil, J. M. (2013). Price Volatility in Food Markets: Can Stock Building Mitigate Price Fluctuations? European Review of Agricultural Economics, 40, 507-528. https://doi.org/10.1093/erae/jbs041
[44]
Shafiee, S., & Topal, E. (2010). A Long-Term View of Worldwide Fossil Fuel Prices. Applied Energy, 87, 988-1000. https://doi.org/10.1016/j.apenergy.2009.09.012
[45]
Shahhosseini, M., Hu, G., & Archontoulis, S. V. (2020). Forecasting Corn Yield with Machine Learning Ensembles. Frontiers in Plant Science, 11, Article No. 1120. https://doi.org/10.3389/fpls.2020.01120
[46]
Sharafi, S., & Nahvinia, M. J. (2024). Sustainability Insights: Enhancing Rainfed Wheat and Barley Yield Prediction in Arid Regions. Agricultural Water Management, 299, Article ID: 108857. https://doi.org/10.1016/j.agwat.2024.108857
[47]
Sun, W., He, Y., & Chang, H. (2015). Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model. Energies, 8, 939-959. https://doi.org/10.3390/en8020939
[48]
Sun, Y., Zhang, X., Wang, H., & Li, J. (2023). Hybrid Forecasting Models for Agricultural Commodity Prices: A Review. Journal of Agricultural Economics, 74, 456-472.
[49]
Thomakos, D., Wood, G., Ioakimidis, M., & Papagiannakis, G. (2023). ShoTS Forecasting: Short Time Series Forecasting for Management Research. British Journal of Management, 34, 539-554. https://doi.org/10.1111/1467-8551.12624
[50]
Toscano, P., Gioli, B., Genesio, L., Vaccari, F. P., Miglietta, F., Zaldei, A. et al. (2014). Durum Wheat Quality Prediction in Mediterranean Environments: From Local to Regional Scale. European Journal of Agronomy, 61, 1-9. https://doi.org/10.1016/j.eja.2014.08.003
[51]
Tudor, C., Sova, R., Stamatiou, P., Vlachos, V., & Polychronidou, P. (2025). Future-Proofing EU-27 Energy Policies with AI: Analyzing and Forecasting Fossil Fuel Trends. Electronics, 14, Article No. 631. https://doi.org/10.3390/electronics14030631
[52]
Verstegen, H., Köneke, O., Korzun, V., & von Broock, R. (2014). The World Importance of Barley and Challenges to Further Improvements. In J. Kumlehn, & N. Stein (Eds.), Bio-Technological Approaches to Barley Improvement. Biotechnology in Agriculture and Forestry (pp. 3-19). Springer. https://doi.org/10.1007/978-3-662-44406-1_1
[53]
Wang, Y., Feng, L., Sui, X., Chu, J., & Mu, L. (2020). Nonlinear Regression Models for Agricultural Price Forecasting: A Comparative Study. Agricultural Systems, 180, 102-115.
[54]
Xiong, T., Li, C., Bao, Y., & Hu, Z. (2015). Forecasting Commodity Futures Prices: A Hybrid VECM-MSVR Approach. Energy Economics, 50, 1-12.
[55]
Zelingher, R., & Makowski, D. (2022). Forecasting Global Maize Prices from Regional Productions. Frontiers in Sustainable Food Systems, 6, Article ID: 836437. https://doi.org/10.3389/fsufs.2022.836437
[56]
Zelingher, R., Makowski, D., & Brunelle, T. (2020). Forecasting Impacts of Agricultural Production on Global Maize Price. CIRED Working Papers, HAL.
[57]
Zhang, D., Chen, S., Liwen, L., & Xia, Q. (2020). Forecasting Agricultural Commodity Prices Using Model Selection Framework with Time Series Features and Forecast Horizons. IEEE Access, 8, 28197-28209. https://doi.org/10.1109/access.2020.2971591
