计算机应用与软件2024，Vol.41Issue(1)：285-290,6.DOI:10.3969/j.issn.1000-386x.2024.01.041
基于集成LSTM自编码器的多维时间序列异常检测
MULTIVARIATE TIME SERIES ANOMALY DETECTION METHOD BASED ON LSTM AUTOENCODER ENSEMBLE
摘要
Abstract
Aimed at the problem that the long-short term memory AutoEncoder(LSTM-AE)is inefficient in anomaly detection on multivariate time series(MTS),a model named LSTM-AE Ensemble(LAE)is proposed.LAE integrated multiple LSTM-AEs to reconstruct each sub-sequence of normal MTS,and treated each reconstruction error as a local feature of the MTS.A fully connected network AutoEncoder(FCAE)was used to fit the reconstruction error data,so that LAE could capture the global features of MTS data.Anomaly detection was carried out according to the reconstruction error of FCAE.Experiments on three public MTS datasets show that compared with the benchmark method,LAE has better MTS anomaly detection performance with the maximum improvement 0.058 4,0.118 4,and 0.078 6 respectively on the terms of Precision,Recall,and F1_score.关键词
多维时间序列/异常检测/LSTM-AE/集成学习Key words
Multivariate time series/Anomaly detection/LSTM-AE/Ensemble learning分类
计算机与自动化引用本文复制引用
李亚静,霍纬纲,丁磊..基于集成LSTM自编码器的多维时间序列异常检测[J].计算机应用与软件,2024,41(1):285-290,6.基金项目
中央高校基本科研业务费项目中国民航大学专项(3122019190). （3122019190）
