When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets
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Computer Science > Machine Learning
arXiv:2606.29248 (cs)
[Submitted on 28 Jun 2026]
Title:When Prices Double in a Week: Forecasting of Agricultural Volatility in Import-Isolated Markets
Authors:Ranuga Weerasekara, Heshan Nethmina, Manuja Ranathunga, Vinma Wettasinghe, Dinithi Navodya, Subavarshana Arumugam, Nirasha Munasinghe, Nisansa de Silva, Sandareka Wickramanayake
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Abstract: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.
| Subjects: | Machine Learning (cs.LG); Methodology (stat.ME) |
| Cite as: | arXiv:2606.29248 [cs.LG] |
| (or arXiv:2606.29248v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.29248
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Ranuga Weerasekara [view email][v1] Sun, 28 Jun 2026 07:30:47 UTC (3,838 KB)
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