计算机科学与探索2024，Vol.18Issue(3)：740-754,15.DOI:10.3778/j.issn.1673-9418.2304005
融合双重注意力机制的时间序列异常检测模型
Time Series Anomaly Detection Model with Dual Attention Mechanism
摘要
Abstract
As an important part of time series research,time series anomaly detection has attracted extensive atten-tion and research in academia and industry.In view of the deep local features and complex dependency in time se-ries data,an anomaly detection model with dual attention mechanism is proposed.The model adopts autoencoder structure.The encoder is composed of a squeeze excitation attention block(SEAB)and a probsparse self-attention block(PSAB).SEAB mines local features containing important information by assigning greater weights to se-quence segments with strong discriminability using dynamic weighted window partitioning.PSAB adopts sparse self-attention mechanism to retain dot products with higher weights,eliminate redundant timing features,and reduce time complexity,so as to capture the long-term dependence of time series.Experimental results show that the pro-posed model achieves the highest F1 score of 0.97 among 9 comparison models and outperforms all other compari-son models in 8 of 14 tested datasets in terms of F1 score,which can effectively identify abnormal situation in time series data and achieve advanced anomaly detection performance.关键词
时间序列/异常检测/深度学习/注意力/自编码器Key words
time series/anomaly detection/deep learning/attention/autoencoder分类
信息技术与安全科学引用本文复制引用
杨超城,严宣辉,陈容均,李汉章..融合双重注意力机制的时间序列异常检测模型[J].计算机科学与探索,2024,18(3):740-754,15.基金项目
国家自然科学基金面上项目(61976053) （61976053）
福建省科技厅引导性项目(2020H0011,2023Y0012).This work was supported by the General Program of National Natural Science Foundation of China(61976053),and the Guided Project of Fujian Provincial Science and Technology Department(2020H0011,2023Y0012). （2020H0011,2023Y0012）
