河南理工大学学报（自然科学版）2025，Vol.44Issue(4)：66-73,8.DOI:10.16186/j.cnki.1673-9787.2024070018
基于提升小波变换的多变量长时间序列预测
Multivariate long-term time series prediction based on the lifting wavelet transform
摘要
Abstract
Objectives To address the challenge of effectively exploiting time-frequency information in multi-variate long-term time series prediction models,this study proposes a neural network model based on multi-level lifting wavelet transform(mLWTNet).Methods The proposed model first applies the lifting wavelet transform to decompose time series data from both time and frequency domains,followed by adaptive filter-ing of the resulting high-frequency subseries.Nonlinear features are extracted using an Elman neural net-work,while linear components are captured with an autoregressive integrated moving average(ARIMA)model.The outputs of the nonlinear and linear predictors are then fused through weighted integration to en-hance prediction accuracy.Results Experiments conducted on five publicly available real-world datasets demonstrate that mLWTNet achieves consistently superior performance—measured by mean squared error(MSE)and mean absolute error(MAE)—across various prediction horizons,outperforming five state-of-the-art models including FEDformer,InParformer,and WaveForM.On average,mLWTNet improves MSE and MAE by approximately 7.15%and 2.43%,respectively,compared with the second-best method.Con-clusions By leveraging lifting wavelet transform and hierarchical reconstruction-based prediction,the pro-posed model effectively utilizes the time-frequency characteristics of time series data,significantly improv-ing forecasting accuracy.关键词
长时间序列预测/时间序列分解/提升小波变换/自适应滤波/Elman神经网络Key words
long-term time series prediction/time series decomposition/lifting wavelet transform/adaptive filtering/Elman neural network分类
计算机与自动化引用本文复制引用
陈旭,张建伟,王叔洋,景永俊..基于提升小波变换的多变量长时间序列预测[J].河南理工大学学报（自然科学版）,2025,44(4):66-73,8.基金项目
中央高校基本科研业务费专项项目(2023ZRLG13) （2023ZRLG13）
宁夏回族自治区重点研发项目(2023BDE02017) （2023BDE02017）
