计算机技术与发展2025，Vol.35Issue(7)：108-116,9.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0037
基于AE-STCN的多元时序异常检测
Multivariate Time Series Anomaly Detection Based on AE-STCN
摘要
Abstract
With the development of sensor technology in the industrial field,the amount and complexity of data have increased rapidly.Traditional anomaly detection methods seem to be inadequate when faced with noise interference and complex data patterns.We propose a multivariate time series anomaly detection model named AE-STCN,which combines the advantages of AutoEncoder(AE)and Symmetric Temporal Convolutional Network(STCN)and jointly uses prediction and reconstruction methods for optimization to capture abnormal patterns more accurately.Among them,the AutoEncoder reconstructs time series by learning the internal structure of data,and the improved Symmetric Temporal Convolutional Network mirrors and flips the input sequence to predict the values at future moments,further capturing the temporal dependencies in the time series.Considering the problem of noise pollution in the training data,we also propose a Transformer-based filter module to effectively reduce the negative influence of noise on model training and enhance the learning ability of normal patterns.To verify the performance of the model,we conduct experiments on the AE-STCN in three public datasets and achieve the best performance under the comprehensive evaluation of AUC,F1,and Fc1 metrics.The results show that AE-STCN outperforms all the baseline models,fully demonstrating the effectiveness and superiority of AE-STCN in processing multivariate time series data and providing a new and reliable solution for multivariate time series anomaly detection.关键词
多元时间序列/异常检测/滤波器/自编码器/时域卷积/联合优化Key words
multivariate time series/anomaly detection/filter/auto-encoder/temporal convolution network/joint optimization分类
信息技术与安全科学引用本文复制引用
洪培林,曾碧卿,刘馨瑶..基于AE-STCN的多元时序异常检测[J].计算机技术与发展,2025,35(7):108-116,9.基金项目
广东省基础与应用基础研究基金项目(2021A1515011171) （2021A1515011171）
广州市基础研究计划、基础与应用基础研究项目(202102080282) （202102080282）
