聊城大学学报（自然科学版）2026，Vol.39Issue(4)：475-485,11.DOI:10.19728/j.issn1672-6634.2026010019
融合时序卷积网络与多头自注意力的时间序列预测模型
Time series prediction model integrating temporal convolutional networks and multi-head self-attention
摘要
Abstract
Traditional forecasting models exhibit notable limitations in capturing long-term dependencies and enhancing the accuracy of multi-step predictions.To further address issues such as gradient vanishing during long-sequence training,low computational efficiency,and error accumulation in multi-step forecas-ting,this paper proposes a time series forecasting model that integrates temporal convolutional networks,a multi-head self-attention mechanism,and a sequence-to-sequence architecture.First,temporal convolu-tional networks are employed to extract local temporal features,leveraging their dilated convolution struc-tures and residual connections to mitigate gradient vanishing and support parallel computation.Then,a multi-head self-attention mechanism is introduced to model global dependencies and enhance contextual semantics based on the outputs of the temporal convolutional networks,forming a"local-global"collabo-rative feature extraction mechanism.Finally,a sequence-to-sequence framework equipped with an atten-tion mechanism maps the enhanced feature sequences into future multi-step price sequences.Experimental results based on daily historical datasets of multiple A-share stocks in the Chinese stock market demon-strate that the proposed model significantly outperforms baseline methods across multiple metrics,with notable reductions in both mean squared error and mean absolute error,validating its effectiveness and su-periority in the task of multi-step stock price forecasting.关键词
时序卷积网络/多头自注意力/序列到序列/股票价格多步预测Key words
temporal convolutional network/multi-head self-attention/sequence to sequence/multi-step stock price forecasts分类
信息技术与安全科学引用本文复制引用
王嵩巍,孙林..融合时序卷积网络与多头自注意力的时间序列预测模型[J].聊城大学学报（自然科学版）,2026,39(4):475-485,11.基金项目
国家自然科学基金项目(62076089) （62076089）
天津市自然科学基金项目(24JCYBJC00890)资助 （24JCYBJC00890）
