较短的长序列时间序列预测模型  

Shorter long-sequence time series forecasting model

作　　者：徐泽鑫 杨磊[1] 李康顺[1] XU Zexin;YANG Lei;LI Kangshun(College of Mathematics and Informatics,South China Agricultural University,Guangzhou Guangdong 510642,China)

机构地区：[1]华南农业大学数学与信息学院,广州510642

出　　处：《计算机应用》2024年第6期1824-1831,共8页journal of Computer Applications

基　　金：广东省自然科学基金资助项目(2020A1515010691);广州市农业科技特派员项目(20212100036)。

摘　　要：针对现有的研究大多将短序列时间序列预测和长序列时间序列预测分开研究而导致模型在较短的长序列时序预测时精度较低的问题,提出一种较短的长序列时间序列预测模型(SLTSFM)。首先,利用卷积神经网络(CNN)和PBUSM(Probsparse Based on Uniform Selection Mechanism)自注意力机制搭建一个序列到序列(Seq2Seq)结构,用于提取长序列输入的特征;其次,设计“远轻近重”策略将多个短序列输入特征提取能力较强的长短时记忆(LSTM)模块提取的各时段数据特征进行重分配;最后,用重分配的特征增强提取的长序列输入特征,提高预测精度并实现时序预测。利用4个公开的时间序列数据集验证模型的有效性。实验结果表明,与综合表现次优的对比模型循环门单元(GRU)相比,SLTSFM的平均绝对误差(MAE)指标在4个数据集上的单变量时序预测分别减小了61.54%、13.48%、0.92%和19.58%,多变量时序预测分别减小了17.01%、18.13%、3.24%和6.73%。由此可见SLTSFM在提升较短的长序列时序预测精度方面的有效性。Aiming at the problem that most of the existing researches study short-sequence time series forecasting and long-sequence time series forecasting separately,which leads to the poor forecasting accuracy of the model in the shorter longsequence time series,a Shorter Long-sequence Time Series Forecasting Model(SLTSFM)was proposed.Firstly,a Sequence-to-Sequence(Seq2Seq)structure was constructed using Convolutional Neural Network(CNN)and PBUSM(Probsparse Based on Uniform Selection Mechanism)self-attention mechanism,which was used to extract the features of the long-sequence input.Secondly,“far light,near heavy”strategy was designed to apply to reallocate the features of each time period extracted from multiple Long Short-Term Memory(LSTM)modules,which were more capable of short-sequence input feature extraction.Finally,the reallocated features were used to enhance the extracted long-sequence input features to improve the forecasting accuracy and realize the time series forecasting.Four publicly available time series datasets were utilized to verify the effectiveness of the proposed model.The experimental results demonstrate that,compared with the suboptimal comprehensive performing model Gated Recurrent Unit(GRU),the Mean Absolute Error(MAE)metrics of SLTSFM were reduced by 61.54%,13.48%,0.92%and 19.58%for univariate time series forecasting,and were reduced by 17.01%,18.13%,3.24%and 6.73%for multivariate time series forecasting on the four datasets.It’s verified that SLTSFM is effective in improving the accuracy of shorter long-sequence time series forecasting.

关 键 词：较短的长序列时间序列预测 序列到序列 长短期记忆 自注意力机制 特征重分配 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]