预测斯里兰卡茶叶市场目录中天气驱动的价格动态
Forecasting Weather-Driven Price Dynamics Across Sri Lankan Tea Market Catalogues
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中文总结 AI 辅助
该研究构建结合105份经纪人报告与区域天气数据的数据集，运用格兰杰因果分析及树模型预测斯里兰卡四大茶叶目录的天气驱动价格，发现低海拔茶等对天气敏感，目录专属建模更优，LightGBM表现突出。
中文摘要 AI 辅助
科伦坡茶叶拍卖（CTA）在决定全球茶叶价格方面发挥着至关重要的作用，但不同茶叶目录下的当地天气状况与价格行为之间的关系尚未得到充分探索。本研究通过提取2023年末至2026年的105份每周经纪人报告中的信息，结合特定区域的天气数据，构建了一个新颖的结构化数据集。我们的分析聚焦于斯里兰卡的四大茶叶目录：高海拔茶、低海拔茶、等外茶和碎茶。为了更好地理解影响茶叶价格的因素，我们将格兰杰因果分析与基于树的机器学习模型（随机森林、XGBoost、LightGBM和梯度提升）结合应用。结果显示，尽管市场动态是主要驱动因素，但天气状况也有显著影响。值得注意的是，低海拔茶在1至3周的滞后时间内对降水量和日照时长表现出强烈敏感性（p<0.05）；等外茶和碎茶目录也对温度变化有显著响应。针对各目录的建模表现优于统一方法，其中LightGBM在四个目录中的三个上表现为最优模型。总体而言，本研究强调在预测茶叶价格时需同时考虑局部天气模式和目录层面的差异，为茶叶行业提供了更精准实用的框架。
英文摘要
The Colombo Tea Auction (CTA) plays a vital role in determining global tea prices, yet the relationship between local weather conditions and price behavior across different tea catalogues has not been thoroughly explored. In this study, we develop a novel, structured dataset by extracting information from 105 weekly broker reports spanning late 2023 to 2026, and combined with region-specific weather data. Our analysis focuses on four main tea catalogues of Sri Lankan tea: High Grown, Low Grown, Off-Grade, and Dust. To better understand the factors influencing tea prices, we apply Granger causality analysis alongside tree-based machine learning models: Random Forest, XGBoost, LightGBM, and Gradient Boosting. Our results show that while market dynamics are primary drivers, weather conditions also have significant effects. Notably, Low Grown tea shows strong sensitivity to precipitation and sunshine duration (p<0.05) across 1-3-week lags. Off-Grade and Dust catalogues also exhibit significant responses to temperature variations. Catalogue-specific modelling outperformed unified approaches, with LightGBM emerging as the superior model for three out of four catalogues. Overall, this study highlights the importance of considering both localized weather patterns and catalogue-level differences when forecasting tea prices, offering a more precise and practical framework for the tea industry.
发表机构
- University of Moratuwa（莫拉图瓦大学）
机构由 AI 辅助整理，请以论文原文为准。
