摘要
随着大数据与物联网技术的迅猛发展,多维时间序列数据的应用范围变得更加广泛。面对大量的非线性、高维冗余特征的复杂时间序列,传统的时间序列分析方法已经不能很好地解决多维时间序列的复杂高维特征问题,从而导致预测效果欠佳。针对以上问题,通过对遗传算法和Informer模型进行改进,并融合GRU网络,提出了GGInformer模型。该模型不仅可以有效提取多维时间序列的关键特征,而且较好地解决了长程依赖问题。为了验证模型的预测能力,选取了2种实际数据集与3种公共基准数据集进行实验,相比较Informer基准模型,GGInformer模型在5种数据集上的MSE分别降低了22%,13%,20%,23%和38%。实验结果表明,GGInformer模型可以有效解决多维时间序列数据的复杂特征提取问题,并可以进一步提高时序预测能力。
With the rapid development of big data and Internet of Things(IoT)technologies,the application scope of multidimensional time series data has become increasingly widespread.Faced with a large amount of complex time series data characterized by non-linearity and high-dimensional redundant features,traditional time series analysis methods struggle to effectively address the complexity of multidimensional time series with high-dimensional features,resulting in suboptimal predictive performance.To address these issues,this paper proposes the GGInformer model,which improves upon the Genetic Algorithm and Informer model while incorporating the GRU network.This model not only efficiently extracts key features from multidimensional time series but also effectively addresses long-term dependency issues.To validate the predictive capability of the model,experiments are conducted on two real datasets and three public benchmark datasets,all of which demonstrated superior performance compared to the baseline models.Specifically,compared to the Informer baseline model,the GGInformer model achieves reductions in Mean Squared Error(MSE)values of 22%,13%,20%,23%,and 38%across the five datasets.The experimental results indicate that the GGInformer model can effectively address the complex feature extraction challenges of multidimensional time series data and further enhance time series prediction capabilities.
作者
任晟岐
宋伟
REN Sheng-qi;SONG Wei(School of Computer and Artificial Intelligence,Zhengzhou University,Zhengzhou 450001,China)
出处
《计算机工程与科学》
CSCD
北大核心
2024年第4期590-598,共9页
Computer Engineering & Science
基金
国家重点研发计划(2023YFC2206400)
河南省高等学校重点科研项目(22A520010)。
关键词
多维时间序列
特征提取
预测
改进遗传算法
multidimensional time series
feature extraction
prediction
improved genetic algorithm
