In recent years, ensuring food security has become a global concern, necessitating accurate forecasting of agriculture security to aid in policymaking and resource allocation. This article proposes the utilization of transformers, a powerful deep learning technique, for predicting the Agriculture Security Index ( ). The is a comprehensive metric that evaluates the stability and resilience of agricultural systems. By harnessing the temporal dependencies and complex patterns present in historical data, transformers offer a promising approach for accurate and reliable forecasting. The transformer architecture, renowned for its ability to capture long‐range dependencies, is tailored to suit the forecasting task. The model is trained using a combination of supervised learning and attention mechanisms to identify salient features and capture intricate relationships within the data. To evaluate the performance of the proposed method, various evaluation metrics, including mean absolute error, root mean square error, and coefficient of determination, are employed to assess the accuracy, robustness, and generalizability of the transformer‐based forecasting approach. The results obtained demonstrate the efficacy of transformers in forecasting the , outperforming traditional time series forecasting methods. The transformer model showcases its ability to capture both short‐term fluctuations and long‐term trends in the , allowing policymakers and stakeholders to make informed decisions. Additionally, the study identifies key factors that significantly influence agriculture security, providing valuable insights for proactive intervention and resource allocation.

中文翻译：

---

预测农业安全指数：变压器方法的证据

近年来，保障粮食安全已成为全球关注的问题，需要对农业安全进行准确预测，以辅助政策制定和资源配置。本文提出利用 Transformer 这种强大的深度学习技术来预测农业安全指数 ( )。这是评估农业系统稳定性和弹性的综合指标。通过利用历史数据中存在的时间依赖性和复杂模式，变压器为准确可靠的预测提供了一种有前景的方法。Transformer 架构以其捕获远程依赖性的能力而闻名，是为适应预测任务而定制的。该模型使用监督学习和注意力机制的组合进行训练，以识别显着特征并捕获数据中复杂的关系。为了评估所提出方法的性能，采用了各种评估指标，包括平均绝对误差、均方根误差和确定系数，来评估基于变压器的预测方法的准确性、鲁棒性和泛化性。获得的结果证明了 Transformer 在预测方面的功效，优于传统的时间序列预测方法。Transformer 模型展示了其捕捉短期波动和长期趋势的能力，使政策制定者和利益相关者能够做出明智的决策。此外，该研究还确定了显着影响农业安全的关键因素，为主动干预和资源分配提供了宝贵的见解。