Forecasting agricultures security indices: Evidence from transformers method

Abstract

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 ( $ASI$). The $ASI$ is a comprehensive metric that evaluates the stability and resilience of agricultural systems. By harnessing the temporal dependencies and complex patterns present in historical $ASI$ 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 $ASI$ 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 $ASI$, outperforming traditional time series forecasting methods. The transformer model showcases its ability to capture both short-term fluctuations and long-term trends in the $ASI$, 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.