Forecasting agricultural markets remains challenging due to nonlinear dynamics, structural breaks, and sparse data. A long-standing belief holds that simple time-series methods outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds with modern time-series foundation models (TSFMs). Using USDA ERS monthly commodity price data from 1997-2025, we evaluate 17 forecasting approaches across four model classes, including traditional time-series, machine learning, deep learning, and five state-of-the-art TSFMs (Chronos, Chronos-2, TimesFM 2.5, Time-MoE, Moirai-2), and construct annual marketing year price predictions to compare with USDA's futures-based season-average price (SAP) forecasts. We show that zero-shot foundation models consistently outperform traditional time-series methods, machine learning, and deep learning architectures trained from scratch in both monthly and annual forecasting. Furthermore, foundation models remarkably outperform USDA's futures-based forecasts on three of four major commodities despite USDA's information advantage from forward-looking futures markets. Time-MoE delivers the largest accuracy gains, achieving 54.9% improvement on wheat and 18.5% improvement on corn relative to USDA ERS benchmarks on recent data (2017-2024 excluding COVID). These results point to a paradigm shift in agricultural forecasting.

翻译：由于非线性动态、结构性断裂和稀疏数据，农业市场预测仍然具有挑战性。长期以来存在一种观点，认为简单的时间序列方法优于更先进的替代方案。本文首次提供了系统性证据，表明这一观点在现代时间序列基础模型（TSFMs）面前已不再成立。利用美国农业部经济研究局（USDA ERS）1997年至2025年的月度大宗商品价格数据，我们评估了跨越四个模型类别的17种预测方法，包括传统时间序列、机器学习、深度学习以及五种最先进的时间序列基础模型（Chronos、Chronos-2、TimesFM 2.5、Time-MoE、Moirai-2），并构建了年度营销年价格预测，以与美国农业部基于期货的季节平均价格（SAP）预测进行比较。我们表明，在月度和年度预测中，零样本基础模型始终优于传统时间序列方法、机器学习以及从头开始训练的深度学习架构。此外，尽管美国农业部拥有来自前瞻性期货市场的信息优势，基础模型在四种主要大宗商品中的三种上显著优于美国农业部基于期货的预测。Time-MoE 带来了最大的准确性提升，在近期数据（2017-2024年，排除COVID期间）上，相对于美国农业部经济研究局的基准，在小麦上实现了54.9%的改进，在玉米上实现了18.5%的改进。这些结果指向了农业预测领域的范式转变。