Kalimati Vegetable Price Index Forecasting with a Momentum Corrected Online Stacking Ensemble
arXiv:2605.30720v1 Announce Type: new Abstract: 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
The development of the Kalimati Vegetable Price Index comes as AI and machine learning techniques mature, allowing for more sophisticated and robust forecasting models in complex economic environments.
This index offers a stable, macro-level signal for agricultural commodity prices in emerging economies, significantly reducing noise and improving forecasting accuracy crucial for policy and market decisions.
The ability to forecast volatile agricultural prices with greater accuracy changes how economic planning, risk management, and food security strategies can be developed, particularly in regions like Nepal.
- · Agricultural commodity traders
- · Nepal's Ministry of Agriculture
- · Emerging market economists
- · Data scientists in economic forecasting
- · Traditional, less data-driven forecasting methods
- · Entities reliant on market volatility for opportunistic gains
Improved price stability for agricultural goods in markets where such indices are adopted.
Better food security planning and reduced inflation volatility in emerging economies.
Enhanced confidence in local agricultural markets, potentially attracting more investment and reducing rural exodus.
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