Research on World Agricultural Economy

Forecasting Agricultural Trade Based on TCN-LightGBM Models: A Data-Driven Decision

Tianwen Zhao

Department of Trade and Logistics, Daegu Catholic University, Gyeongsan 38430, Republic of Korea

Guoqing Chen

Mathematical Modeling Research Center, Chengdu Jincheng College, Chengdu 611731, China;  Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, Thailand

Thom Gatewongsa

The Research Institute of Northeastern Art and Culture, Mahasarakham University, Maha Sarakham 44150, Thailand

Piyapatr Busababodhin

Department of Mathematics, Faculty of Science, Mahasarakham University, Maha Sarakham 44150, Thailand

DOI: https://doi.org/10.36956/rwae.v6i1.1429

Received: 28 October 2024; Published: 16 January 2025

Abstract

Facing the increasing complexity and dynamic fluctuations of the global agricultural trade market, accurate forecasting plays a key role in supporting agricultural policy formulation, stabilising the market and optimising resource allocation. In order to increase the precision and stability of agricultural trade predictions, this research suggests a hybrid model built on a temporal convolution network (TCN) and a lightweight gradient boosting tree (LightGBM). The TCN module effectively captures the long-term dependence characteristics of time series data through dilated convolution, which improves the model’s ability to identify seasonal and periodic trends. The LightGBM module, on the other hand, makes use of the characteristics of gradient boosting decision trees and excels at efficiently handling nonlinear relationships and avoiding overfitting. Experimental results show that the TCN-LightGBM model outperforms traditional models in terms of mean square error (MSE), mean absolute error (MAE) and prediction accuracy. Specifically, compared with ARIMA, LSTM, TCN alone or LightGBM alone, TCN-LightGBM achieves a prediction accuracy of 91.3% on the test data, with MSE and MAE of 0.021 and 0.115 respectively, significantly improving prediction accuracy and stability. In addition, parameter sensitivity analysis shows that the TCN-LightGBM model maintains a highly consistent prediction trend under different parameter configurations, which verifies the robustness of the model and its practical application value. This study provides a data-driven decision support tool with high accuracy and strong stability, providing a new solution for agricultural trade forecasting and other complex time series prediction tasks.

Keywords: Agricultural Trade Forecasting; TCN; LightGBM; Data-Driven; Decision Support

References