北京师范大学学报（自然科学版）2025，Vol.61Issue(6)：805-814,10.DOI:10.12202/j.0476-0301.2025140
融合特征协同筛选与时序深度学习的棉花期货价格预测方法
A cotton futures price prediction method integrating feature synergistic selection with temporal deep learning
摘要
Abstract
Accurately predicting cotton futures prices is challenging due to difficulties in fusing multi-source heterogeneous data with inefficiency in feature extraction.To address this,a multimodal data fusion framework driven by a feature co-selection mechanism and a bidirectional long short-term memory(BLSTM)network is proposed.This framework integrates multi-source information,including futures market indicators,remote sensing image features,and investor sentiment derived from textual data.The feature co-selection mechanism facilitates hierarchical dimensionality reduction,while BLSTM captures nonlinear temporal dependencies.Our experiments validate the effectiveness of multi-source data fusion in elucidating complex drivers of price fluctuations.Our method significantly enhances prediction accuracy,reducing the root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and normalized root mean square error(NRMSE)by 49.11%,56.16%,11.21%,and 14.47%,respectively,compared to a BLSTM model trained on non-integrated data.Feature analysis reveals that historical futures prices,market sentiment indices,and remote sensing vegetation features all contribute to the prediction.The present work offers novel insights regarding analysis of agricultural financial derivatives,also provides empirical reference for applications of multimodal data in financial modeling.关键词
棉花期货价格预测/多源异构数据/多模态数据融合/特征协同筛选/双向长短期记忆网络Key words
cotton futures price prediction/multi-source heterogeneous data/multimodal data fusion/feature co-selection/bidirectional long short-term memory分类
管理科学引用本文复制引用
王俊,王有钰,马丁惠子,冯思豪,熊杰..融合特征协同筛选与时序深度学习的棉花期货价格预测方法[J].北京师范大学学报（自然科学版）,2025,61(6):805-814,10.基金项目
国家自然科学基金资助项目(72471197) （72471197）
四川省哲学社会科学基金资助项目(SCJJ25ND091) （SCJJ25ND091）
