Price volatility in global food commodities is a critical signal indicating potential disruptions in the food market. Understanding forthcoming changes in these prices is essential for bolstering food security, particularly for nations at risk. The Food and Agriculture Organization of the United Nations (FAO) previously developed sophisticated statistical frameworks for the proactive prediction of food commodity prices, aiding in the creation of global early warning systems. These frameworks utilize food security indicators to produce accurate forecasts, thereby facilitating preparations against potential food shortages. Our research builds on these foundations by integrating robust price security indicators with cutting-edge deep learning (DL) methodologies to reveal complex interdependencies. DL techniques examine intricate dynamics among diverse factors affecting food prices. Through sophisticated time-series forecasting models coupled with a classification model, our approach enhances existing models to better support communities worldwide in advancing their food security initiatives.

利用深度学习技术的时间序列预测模型，结合分类模型，提升已有模型以支持全球社区在推进食品安全倡议方面的能力。

养料网络：全球警示系统中食品商品价格的预测性严重状态