用机器学习预测斯里兰卡蔬菜价格波动，考虑季节与供应链因素。
When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets
- 构建融合天气、油价、汇率的多源数据模型，分季节建模
- 统一模型预测准确率达90.84%，2024年超通胀期仍保持85.96%准确率
- 首次在进口受限市场中实现跨周期价格预警，适合政策制定者使用
斯里兰卡蔬菜价格高度波动，因市场高度依赖进口隔离。本研究构建机器学习框架，结合供应链感知特征与双季种植周期（Maha：10月–4月，Yala：5月–9月），整合2013–2019年12种蔬菜、14个市场的零售价、产地价、气候变量、柴油成本及汇率数据。采用XGBoost与LightGBM梯度提升集成模型，通过Optuna优化，对比统一模型与分季模型。结果表明，分季模型提升季内拟合度，其中Yala季模型达最高R² 0.9420（95% CI [0.690, 1.000]）；统一模型整体预测准确率90.84%（95% CI [88.34%, 91.52%]），R²为0.9281（95% CI [0.760, 1.000]）。该模型在未训练的2024年超通胀期仍保持85.96%准确率，成功追踪重大价格飙升。研究表明，在捕捉供应链动态的前提下，进口受限市场的农产品价格可有效预测，为农户、贸易商与政策制定者提供早期预警与决策支持。现有研究仅限于单市场ARIMA/GARCH模型，缺乏供应链特征、季节划分与跨周期验证。
原文摘要 · Abstract (English)
Vegetable prices in Sri Lanka are highly volatile because the market is largely import-isolated, so supply disruptions quickly drive prices up. This study develops a machine learning framework to forecast such volatility by incorporating supply-chain-aware features and explicitly modelling the country's two cultivation seasons, Maha (October-April) and Yala (May-September). An integrated dataset was constructed by combining retail and farmer-gate prices with origin-aligned weather variables, diesel costs, and exchange rates across 12 vegetable varieties and 14 market centres from 2013 to 2019. A gradient-boosted ensemble model (XGBoost and LightGBM) was trained and optimised using Optuna, and unified and season-specific configurations were compared. Results show that season-specific models improve within-season fit, with the Yala-specific model achieving the highest R2 of 0.9420 (95% CI [0.690, 1.000]), while the unified model delivers the best overall predictive accuracy of 90.84% (95% CI [88.34%, 91.52%]) and an R2 of 0.9281 (95% CI [0.760, 1.000]). Notably, the unified model maintains 85.96% accuracy on a completely unseen 2024 hyperinflationary period without retraining, successfully tracking major price surges. These findings suggest that agricultural price movements in import-constrained markets are meaningfully predictable when models capture supply-chain dynamics, offering practical value for early warning and decision making by farmers, traders, and policymakers. Existing studies on Sri Lankan vegetable prices are confined to Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) applied to single markets, with no supply-chain features, seasonal segmentation, or cross-regime validation.
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