基于季节分解的混合神经网络的时间序列预测  

Time Series Prediction of Hybrid Neural Networks Based on Seasonal Decomposition

作　　者：徐筠雯 陈宗镭 李天瑞[1] 李崇寿 XU Junwen;CHEN Zonglei;LI Tianrui;LI Chongshou(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学计算机与人工智能学院,成都611756

出　　处：《计算机科学》2024年第S02期543-549,共7页Computer Science

基　　金：国家自然科学基金(62202395,62176221);四川省自然科学基金(2022NSFSC0930);中央高校基本科研业务费专项资金(2682022CX067)。

摘　　要：近年来,时间序列预测已经在金融、气象、军事等多个领域得到广泛应用。深度学习已开始在时间序列预测任务中展现巨大的潜力和应用前景。其中,循环神经网络在跨度较大的时间序列预测中容易出现信息丢失和梯度爆炸等问题。而Transformer模型及其变种在使用注意力机制时通常忽略了时间序列变量之间的时序关系。为了应对这些问题,提出了一种基于季节分解的混合神经网络时间序列预测模型。该模型利用季节分解模块来捕获时间序列中不同周期频率分量的变化,同时通过融合多头注意力机制和复合扩张卷积层,利用全局信息和局部信息的交互获取数据之间的多尺度时序位置信息。最终,在4个领域的公开数据集上进行了实验,结果表明模型的预测性能优于当前的主流方法。In recent years,time series forecasting has found widespread applications in various domains such as finance,meteoro-logy,and military.Deep learning has begun to demonstrate significant potential and application prospects in time series forecasting tasks.However,recurrent neural networks often encounter issues like information loss and exploding gradients when dealing with time series predictions over extended periods.In contrast,Transformer models and their variants,when utilizing attention mechanisms,typically overlook the temporal relationships between variables in time series data.To address these challenges,this paper proposes a hybrid neural network time series forecasting model based on seasonal decomposition.This model employs a seasonal decomposition module to capture the variations in different periodic frequency components within the time series.Simultaneously,by integrating multi-head self-attention mechanisms and composite dilated convolution layers,the model leverages the interaction between global and local information to obtain multi-scale temporal positional information among the data.Ultimately,experiments are conducted on publicly available datasets from 4 different domains,and the results indicate that the predictive perfor-mance of the proposed model surpasses that of current popular mainstream methods.

关 键 词：时间序列预测 季节分解 注意力机制 扩张卷积 混合模型 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]