计算机工程与应用2026，Vol.62Issue(8)：366-379,14.DOI:10.3778/j.issn.1002-8331.2501-0225
基于TA-Informer模型的多元长期时间序列预测研究
Multivariate Long-Term Time Series Prediction Based on TA-Informer Model
摘要
Abstract
In multivariate long-term time series forecasting,the feature redundancy and long-term dependency of the data are difficult to capture,which becomes a key problem affecting the forecasting accuracy.In order to improve the multi-variate long-term time series prediction accuracy,a multivariate long-term time series prediction model based on TA-Informer is proposed.Firstly,the model uses temporal convolutional network(TCN)to extract features from multi-variate long-term time series for capturing long-term dependencies.Then,the model feeds the extracted features into an adaptive sparse self-attention(ASSA)to eliminate redundant features and enhance the important features.Finally,the model feeds the enhanced important features into the Informer module to realize the multivariate long-term time series prediction task.The experimental results show that TA-Informer reduces the MSE on six public datasets by 57.5%,25.8%,50.3%,60%,48.1%and 45.2%,respectively,compared with the benchmark model Informer,which reflects the effectiveness and feasibility of the scheme.关键词
多元长期预测/深度学习/特征提取/冗余特征/时间卷积网络/自适应稀疏自注意力/InformerKey words
multivariate long-term time series prediction/deep learning/feature extraction/redundancy feature/temporal convolutional network/adaptive sparse self-attention/Informer分类
信息技术与安全科学引用本文复制引用
王新科,梅红岩,赵勤,翟心晨,赵恩童..基于TA-Informer模型的多元长期时间序列预测研究[J].计算机工程与应用,2026,62(8):366-379,14.基金项目
国家自然科学基金(12371363) （12371363）
辽宁省科技计划联合计划(重点研发计划项目)(2025JH2/101800245). （重点研发计划项目）
