When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets
一周价格翻倍：进口隔离市场中的农业价格波动预测AI 解读 ↓
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一周价格翻倍：进口隔离市场中的农业价格波动预测
- 研究问题
- 在进口受限、供给冲击容易引发价格跳升的斯里兰卡蔬菜市场，供应链信息和种植季节能否帮助预测价格变化？
- 方法与思路
- 作者整合价格、产地天气、柴油成本和汇率，用 Optuna 调优 XGBoost 与 LightGBM，并比较统一及分季模型。
- 材料陈述的贡献
- 摘要报告分季模型改善季内拟合，统一模型整体预测更好，并在未重新训练的情况下对未见过的 2024 年时期进行验证。
- 阅读关注点
- 应核对预测目标与期限、准确率定义、时间切分和特征可得性，以及置信区间计算和跨时期验证是否存在信息泄漏。
- 阅读边界
- 来源为 arXiv 预印本，同行评审与期刊接收未核实；摘要未解释准确率口径，也未交代异常时期标签和预测误差分布。
原文摘要
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.