Ibañez, S.C.; Monterola, C.P. A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers. Agriculture2023, 13, 1855.
Ibañez, S.C.; Monterola, C.P. A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers. Agriculture 2023, 13, 1855.
Ibañez, S.C.; Monterola, C.P. A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers. Agriculture2023, 13, 1855.
Ibañez, S.C.; Monterola, C.P. A Global Forecasting Approach to Large-Scale Crop Production Prediction with Time Series Transformers. Agriculture 2023, 13, 1855.
Abstract
Accurate prediction of crop production is essential in effectively managing agricultural countries' food security and economic resilience. This study evaluates the performance of statistical and machine learning-based methods for large-scale crop production forecasting. We predict the quarterly production of 325 crops (including fruits, vegetables, cereals, non-food, and industrial crops) across 83 provinces in the Philippines. Using a comprehensive dataset of 10,949 time series over 13 years, we demonstrate that a global forecasting approach using a state-of-the-art deep learning architecture, the transformer, significantly outperforms traditional local forecasting approaches built on statistical and baseline methods. By leveraging cross-series information, our proposed way is scalable and works well even with time series that are short, sparse, intermittent, or exhibit structural breaks/regime shifts. The results of this study further advance the field of applied forecasting in agricultural production and provide a practical and effective decision-support tool for policymakers that oversee the farm sector on a national scale.
Keywords
crop production; agricultural production; time series forecasting; artificial intelligence; transformer; machine learning; deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
