计算机科学与探索2025，Vol.19Issue(8)：2099-2109,11.DOI:10.3778/j.issn.1673-9418.2410089
基于时域频域混合特征的多变量时序预测模型
Multivariate Time Series Prediction Model Based on Mixed Features of Time Domain and Frequency Domain
摘要
Abstract
Currently,multivariate time series prediction methods mainly transform time series into frequency domain repre-sentations for feature extraction.But this leads to the loss of some time domain information and accuracy,and the traditional attention mechanism brings quadratic time complexity.To address these issues,a multivariate time series prediction model(TFMformer)based on hybrid time-frequency domain features is proposed.TFMformer uses a multi-scale segmentation operation to decompose more accurate semantics from a multi-scale perspective,enhancing the model's ability to capture comprehensive semantic information of the series.It reduces the number of input tokens through slicing to lower time complexity.A hybrid time-frequency domain feature enhancement module is introduced to make time domain and frequency domain features fuse and interact,improving overall feature representation.Additionally,time domain feature information is incorporated into frequency domain attention to enhance the frequency domain's perception of time domain information,enabling the model to focus more precisely on meaningful feature combinations and reducing prediction bias due to the lack of time domain information.TFMformer is tested on six benchmark datasets.Compared with existing advanced methods,the mean squared error and mean absolute error of the prediction results are decreased by an average of 3.8%and 2.8%respectively,and the maximum reduction in mean absolute error reaches 11.2%,which proves the effectiveness of model.关键词
多变量时间序列预测/Transformer/序列分解/频域注意力机制/深度学习Key words
multivariate time series prediction/Transformer/sequence decomposition/frequency domain attention mech-anism/deep learning分类
信息技术与安全科学引用本文复制引用
闵锋,刘宇卓,刘煜晖,刘彪..基于时域频域混合特征的多变量时序预测模型[J].计算机科学与探索,2025,19(8):2099-2109,11.基金项目
国家自然科学基金(62171328) （62171328）
武汉工程大学研究生教育创新基金(CX2024144).This work was supported by the National Natural Science Foundation of China(62171328),and the Postgraduate Education Innovation Fund of Wuhan Institute of Technology(CX2024144). （CX2024144）
