用新指数和集成模型精准预测尼泊尔蔬菜价格波动。
Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble
- 构建135种蔬菜的加权价格指数，降低个体作物噪声。
- 90天预测误差仅0.68% MAPE，R²达0.845，表现最优。
- 适合政策制定者与供应链管理者用于保障粮食安全。
在新兴经济体中，农产品价格预测因高波动性、频繁供应中断及文化因素对需求的影响而困难重重。本研究提出卡尔马蒂蔬菜价格指数（KVPI），一个基于反波动性加权的复合指数，整合了2013至2023年十年间加德满都135种日批发商品数据。通过构建稳定的宏观信号，该指数有效降低了单一作物建模中的噪声。研究开发了64个因果有效的特征，包括节日前后效应、滚动统计量和日历变量。评估了14种涵盖统计、树模型、深度学习、混合及变压器架构的模型，在7天、14-30天及90天三种时间尺度下的表现。树模型集成展现出显著稳健性，而传统统计模型与复杂变压器在噪声数据上表现不佳。所提出的动量修正在线堆叠集成模型在90天预测中达到1.771的均方根误差（RMSE）、0.68%的平均绝对百分比误差（MAPE），并解释了84.5%的方差（R² = 0.845）。该开源流程为尼泊尔及类似市场中的政策制定者与供应链参与者提供了一套实用可靠的定价预测工具，助力提升粮食安全。
原文摘要 · Abstract (English)
Forecasting agricultural commodity prices in emerging economies is difficult due to high volatility, frequent supply disruptions, and strong cultural influences on demand. This study introduces the Kalimati Vegetable Price Index (KVPI), a new inverse-volatility weighted composite index that aggregates 135 daily wholesale commodities from Kathmandu over ten years (2013-2023). By creating a stable macro-level signal, the KVPI reduces the noise inherent in modelling individual crops. A rich set of 64 causally valid features was developed, including festival lead-lag effects, rolling statistics, and calendar variables. Fourteen forecasting models spanning statistical, tree-based, deep learning, hybrid, and transformer architectures were rigorously evaluated across short (7-day), medium (14- and 30-day), and long-term (90-day) horizons. Tree-based ensembles proved notably robust, while classical statistical models and complex transformers struggled with the noisy dataset. The proposed Momentum-Corrected Online Stacking Ensemble achieved the strongest performance, yielding a Root Mean Square Error (RMSE) of 1.771, an exceptionally low Mean Absolute Percentage Error (MAPE) of 0.68%, and explaining 84.5% of the variance (R-squared = 0.845) at the 90-day horizon. This open-source pipeline provides policymakers and supply chain actors in Nepal and similar markets with a practical, reliable tool for anticipating price movements and strengthening food security.
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