基于多尺度特征融合的时间序列长期预测模型  

Long-term prediction model of time series based on multi-scale feature fusion

作　　者：刘文博 于连飞 谢冬梅 蔡闯 曲志坚[1] 任崇广 LIU Wenbo;YU Lianfei;XIE Dongmei;CAI Chuang;QU Zhijian;REN Chongguang(School of Computer Science and Technology,Shandong University of Technology,Zibo Shandong 255049,China;Jinan Inspur Data Technology Company Limited,Jinan Shandong 250000,China)

机构地区：[1]山东理工大学计算机科学与技术学院,山东淄博255049 [2]济南浪潮数据技术有限公司,济南250000

出　　处：《计算机应用》2024年第11期3435-3441,共7页journal of Computer Applications

基　　金：山东省高等学校青年创新团队发展计划项目(2019KJN48)。

摘　　要：长期时间序列预测在多个领域中具有广泛的应用需求。但是,时间序列的长期预测过程中表现出的非平稳性问题是影响预测准确性的关键因素。为了提高时间序列长期预测精度,以及预测模型的普适性,构建了基于序列分解的多尺度融合注意力神经网络预测网络(MSDFAN)模型。该模型采用时间序列分解提取输入数据中的季节成分和趋势成分,对不同数据成分进行不同的预测建模,能够对具有多尺度稳定特征的非平稳时间成分进行建模和预测。实验结果表明,与FEDformer相比,MSDFAN在5个基准数据集上的预测结果的均方误差(MSE)和平均绝对误差(MAE)分别平均下降了12.95%和8.49%,MSDFAN模型在多变量时间序列上取得了更好的预测精度。Long-term time series prediction has a wide range of application requirements in many fields.However,the non-stationarity problem shown in the long-term prediction process of time series is a key factor affecting the prediction accuracy.To improve the long-term prediction accuracy of time series and the universality of prediction model,a Multi-Scale Decomposition Fusion Attention Network(MSDFAN)was constructed.The model uses time series decomposition to extract seasonal components and trend components in the input data,and models different predictions for different data components,and is able to model and predict non-stationary time components with multi-scale stability characteristics.Experimental results show that compared with FEDformer,the Mean Squared Error(MSE)and Mean Absolute Error(MAE)of MSDFAN on five benchmark datasets are reduced by 12.95%and 8.49%,averagely and respectively.MSDFAN achieves a better prediction accuracy on multivariate time series.

关 键 词：长期预测 深度学习 序列分解 多特征融合 非平稳性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]