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<p>Projecting composite wholesale agricultural commodity price indices within the Vegetable Basket Project equips policymakers and market participants with empirical tools for optimizing production scheduling, procurement decision-making, and risk management by forecasting volatility and directional shifts. Such precise index estimation enhances supply chain efficiency and reduces financial risk through informed hedging strategies. Here, we introduce a novel forecasting architecture employing Gaussian process regression (<i>GPR</i>) optimized within a Bayesian inference framework, enabling the model to adjust to latent market forces and sudden structural transitions. Integrating time-varying dynamics allows the algorithm to more faithfully reproduce nonstationary price trajectories. The empirical component analyzes daily observations spanning September 27, 2005–April 10, 2025, encompassing eras of regulatory change, sectoral transformation, and macroeconomic upheaval. In an out-of-sample validation from February 17, 2022 to April 10, 2025, our GPR model achieves a relative root mean square error (<i>RRMSE</i>) of 0.2040 percent, a <i>RMSE</i> of 0.4841, a <i>MAE</i> of 0.3430, and a correlation coefficient (<i>CC</i>) of 0.99929. To the best of our awareness, this research constitutes the inaugural implementation of a Bayesian-tuned <i>GPR</i> methodology for composite wholesale agricultural price forecasting. By contributing to machine learning-based predictive theory, this flexible framework can be adapted to similarly complex time-series tasks in other commodity domains.</p>

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Machine learning-driven forecasting of composite wholesale price indices for agricultural commodities in the Vegetable Basket Project

  • 期刊论文

  • 发表日期：

    2026年3月14日

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  • Bingzi Jin,
  • Xiaojie Xu

摘要

Projecting composite wholesale agricultural commodity price indices within the Vegetable Basket Project equips policymakers and market participants with empirical tools for optimizing production scheduling, procurement decision-making, and risk management by forecasting volatility and directional shifts. Such precise index estimation enhances supply chain efficiency and reduces financial risk through informed hedging strategies. Here, we introduce a novel forecasting architecture employing Gaussian process regression (GPR) optimized within a Bayesian inference framework, enabling the model to adjust to latent market forces and sudden structural transitions. Integrating time-varying dynamics allows the algorithm to more faithfully reproduce nonstationary price trajectories. The empirical component analyzes daily observations spanning September 27, 2005–April 10, 2025, encompassing eras of regulatory change, sectoral transformation, and macroeconomic upheaval. In an out-of-sample validation from February 17, 2022 to April 10, 2025, our GPR model achieves a relative root mean square error (RRMSE) of 0.2040 percent, a RMSE of 0.4841, a MAE of 0.3430, and a correlation coefficient (CC) of 0.99929. To the best of our awareness, this research constitutes the inaugural implementation of a Bayesian-tuned GPR methodology for composite wholesale agricultural price forecasting. By contributing to machine learning-based predictive theory, this flexible framework can be adapted to similarly complex time-series tasks in other commodity domains.