一种混合TCN-XGBoost模型用于农产品市场价格预测
Tianwen Zhao1, Guoqing Chen2,3, Sujitta Suraphee3,4
1Department of Trade and Logistics, Daegu Catholic University, Gyeongsan, Republic of Korea.
PloS one
|May 2, 2025
概括
本研究引入了一种新的TCN-XGBoost模型,用于准确预测农产品价格波动. 综合方法显著优于传统方法,为市场决策和风险管理提供了可靠的预测.
科学领域:
- 农业经济学 农业经济学
- 数据科学数据科学数据科学
- 时间序列分析时间序列分析
背景情况:
- 农业价格波动是由季节性,供需动态,政策转变和气候变化所驱动的.
- 传统的时间序列模型与农业价格波动的非线性性质作斗争.
- 准确的预测对于市场决策和农业风险管理至关重要.
研究的目的:
- 开发和评估一个综合预测模型,将时间卷积网络 (TCN) 和XGBoost结合起来,以改进农业价格波动的预测.
- 解决线性模型在捕捉复杂,非线性价格动态方面的局限性.
- 提高农业价格预测的准确性和稳定性.
主要方法:
- 开发了一种综合模型,将时间卷积网络 (TCN) 用于时间特征提取和XGBoost用于非线性关系建模.
- 应用了滑窗技术对65,750个历史数据点的米,小麦和玉米价格来构建时间序列特征.
- 在TCN-XGBoost模型的性能与传统模型 (ARIMA,LSTM) 和其他混合模型 (变压器-XGBoost,CNN-XGBoost) 相比进行了基准测试.
主要成果:
- 该TCN-XGBoost模型实现了优异的性能,RMSE为0.26和MAPE为5.3%,表现优于ARIMA (RMSE=0.36,MAPE=8.9%) 和LSTM (RMSE=0.34,MAPE=8.1%).
- 它还超过了其他混合型号,包括变压器-XGBoost (RMSE=0.23) 和CNN-XGBoost (RMSE=0.29).
- 该模型在价格大幅波动时表现强 (RMSE=0.28,MAPE=6.1%),有效地捕捉趋势和幅度.
结论:
- 集成的TCN-XGBoost模型为预测农产品价格波动提供了一种高效,强大的解决方案.
- 它能够捕捉短期/长期依赖和复杂的非线性,使其非常有效.
- 该模型为加强农业市场的决策和风险管理策略提供了巨大的潜力.
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