AgriVolT: A Multi-Modal Temporal Vision Transformer for Climate-Informed Commodity Price Forecasting (Papers Track)
Sharanya Roy (Algoverse); Krisha Agarwal (Algoverse); Sahir Gupta (Algoverse); Anshul Patil (Algoverse); Ahan M R (Algoverse)
Abstract
Climate extremes increasingly disrupt agriculture, driving volatility in staple food markets and threatening global food security. We present AgriVolT (Agriculture Volatility Transformer), a multi-modal temporal vision transformer that integrates climate reanalysis, satellite imagery, production data, and historical market prices to forecast commodity price fluctuations. AgriVolT employs temporal encodings for economic sequences, a price-focused prediction head, and cross-modal attention to capture non-linear interactions between climate shocks and market dynamics. In validation experiments on U.S. corn, soybean, and wheat markets, AgriVolT reduced mean absolute error (MAE) by 30–50% and mean absolute percentage error (MAPE) by up to 60% compared to AgriFM, a recent model we used to benchmark AgriVolT with. For example, it achieved a 37.1% MAPE for corn (vs. 74.8%) and 38.2% for wheat (vs. 98.7%). These results highlight AgriVolT’s potential to provide accurate, interpretable early warnings that help policymakers, humanitarian organizations, and farmers mitigate food insecurity in climate-volatile markets.