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arXiv Machine Learning By Hesandi Mallawarachchi, Senilka Madurapperumage, Nadil Kulathunge, Thilokya Angeesa, Nethsith Gunaweera, Sandeepa Weerasekara, Patalee Narasinghe, Nisansa de Silva, Sandareka Wickramanayake

Forecasting Weather-Driven Price Dynamics Across Sri Lankan Tea Market Catalogues

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The study investigates how local weather conditions influence price dynamics in Sri Lankan tea markets by creating a structured dataset from 105 weekly broker reports and regional weather data. Using Granger causality and tree‑based machine learning models, the authors find that market forces dominate but weather—especially precipitation and sunshine for Low Grown tea, and temperature for Off‑Grade and Dust—significantly affects prices with notable lag effects. Catalogue‑specific models, particularly LightGBM, outperform unified approaches, underscoring the value of tailored forecasting.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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