When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets
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,...
| Authors | Ranuga Weerasekara, Heshan Nethmina, Manuja Ranathunga et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 June 2026 |
| Last updated at source | 14 August 2026 |
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