基于XGBoost和TCN-Attention的棉花价格多影响因素选择及预测  

Selection and Prediction of Multiple Influencing Factors of Cotton Price Based on XGBoost and TCN-Attention

作　　者：王世杰 王兴芬[1,3] 岳婷 WANG Shi-Jie;WANG Xing-Fen;YUE Ting(Institute of Business Intelligence,Beijing Information Science&Technology University,Beijing 100192,China;School of Computer,Beijing Information Science&Technology University,Beijing 100192,China;School of Information Management,Beijing Information Science&Technology University,Beijing 100192,China)

机构地区：[1]北京信息科技大学商务智能研究所,北京100192 [2]北京信息科技大学计算机学院,北京100192 [3]北京信息科技大学信息管理学院,北京100192

出　　处：《计算机系统应用》2023年第10期10-21,共12页Computer Systems & Applications

基　　金：国家重点研发计划(2019YFB1405003)。

摘　　要：棉花价格受多种因素影响而复杂多变,通过选择合适的数据特征和预测模型可提高棉花价格预测精度.本文以棉花日现货价格数据为研究目标,采集了供需关系、国际市场、宏观经济、产业链这4个方面的9项影响因素作为特征,使用极限梯度提升(XGBoost)算法对棉花价格影响因素进行特征评估筛选,选取其中5项特征后,采用引入注意力机制(Attention)的时间卷积网络(TCN) TCN-Attention、TCN、LSTM、GRU等模型对棉花价格进行预测.通过消融实验和对比实验,结果表明:(1)经过XGBoost特征筛选后, TCN-Attention价格预测的平均绝对误差(MAE)和均方根误差(RMSE)为41.47和58.76,与未筛选相比分别降低了77.57%和76.49%.(2)与TCN、LSTM、GRU相比,本文提出的TCN-Attention模型预测结果更准确, MAE和RMSE均降低50%以上,运行时间较LSTM、GRU缩短60%.Cotton price is complex and changeable due to many factors,and the prediction accuracy of cotton price can be improved by selecting appropriate data features and prediction models.In this study,the daily spot price data of cotton are taken as the research target,and nine influencing factors in four aspects of supply and demand,international market,macroeconomy,and industrial chain are collected as features.The extreme gradient boosting(XGBoost)algorithm is used to evaluate and screen the influencing factors of cotton price,and five of them are selected.This study adopts the time convolution network(TCN)with an attention mechanism(Attention),namely TCN-Attention,TCN,long short-term memory(LSTM),gate recurrent unit(GRU),and other models to predict cotton price.Through ablation experiments and comparative experiments,the results show that:(1)After XGBoost feature screening,the mean absolute error(MAE)and root mean square error(RMSE)of TCN-Attention price prediction are 41.47 and 58.76,which are 77.57% and 76.49%lower than those before screening;(2)compared with TCN,LSTM,and GRU,the TCN-Attention model proposed in this study has more accurate prediction results.MAE and RMSE are reduced by more than 50%,and the running time is shortened by 60% compared with LSTM and GRU.

关 键 词：价格预测 XGBoost TCN ATTENTION 消融实验 

分 类 号：F323.7[经济管理—产业经济] F224