西安电子科技大学学报（自然科学版）2025，Vol.52Issue(2)：128-142,15.DOI:10.19665/j.issn1001-2400.20241207
基于多尺度时频域学习的多元长时间序列预测
Multivariate long-term series forecasting based on multi-scale time-frequency domain learning
摘要
Abstract
To address two key issues in existing multivariate long-term series forecasting models—namely,the inability to capture long-term dependencies using single-period scale time-domain information,and the difficulty in capturing effective multivariate dependencies—a multivariate long-term series forecasting model based on multi-scale time-frequency domain learning is proposed by utilizing multilayer perceptrons.The model first employs the Fourier transform to adaptively identify different periods of the sequence as multiple scales.Then,for each scale,the sequence is decomposed to conduct two-stage learning in both the time and frequency domains,capturing local and global temporal dependencies.Subsequently,based on the results of correlation analysis among the variables,the model adaptively constructs the variable dependencies within the multivariate time series.Finally,different aggregation methods are applied to the decomposed components of the sequence across different scales to achieve complementary integration of multi-scale information.Experiments on seven real-world datasets demonstrate that this model achieves an optimal or suboptimal performance in over 90％of tests.Compared to the linear model DLinear based on sequence decomposition,the proposed model achieves an average reduction of 11％and a maximum reduction of 49.22％in MSE,as well as an average reduction of 10％and a maximum reduction of 33.03％in MAE.Furthermore,the model enhances the forecasting accuracy while also demonstrating an advanced operational efficiency.关键词
预测/时间序列/时频域/多尺度/序列分解/多层感知机Key words
forecasting/time series/time-frequency domain/multi-scale/sequence decomposition/multilayer perceptrons分类
计算机与自动化引用本文复制引用
衡红军,李怡欣..基于多尺度时频域学习的多元长时间序列预测[J].西安电子科技大学学报（自然科学版）,2025,52(2):128-142,15.基金项目
国家自然科学民航联合研究基金(U2333204) （U2333204）
