摘要
针对传统异常点检测模型难以准确分析汽车驾驶异常行为的情况,建立一种基于自动编码器与孤立森林算法的多维时间序列汽车驾驶异常点检测模型。利用滑动窗口计算原始多维时间序列范数、范数变化率及相关统计信息值提取数据特征,通过自动编码器重构特征数据,并结合孤立森林算法实现异常点检测。实验结果表明,与基于LOF、OCSVM、iForest和LSTM-AE的异常点检测模型相比,该模型的召回率和F1度量值可分别提升至6%和2.4%以上,综合性能更优。
Existing outlier detection models cannot accurately analyze abnormal driving behavior.To address the problem,this paper builds a driving outlier detection model using multidimensional time series based on an autoencoder and the isolation forest algorithm.The model uses sliding windows to calculate the norm of the original multidimensional time series,the change rate of the norm and values of related statistical information to extract data features.Feature data is reconstructed using an autoencoder,and on this basis the isolation forest algorithm is used to realize outlier detection.Experimental results show that the proposed model generally outperforms other outlier detection models such as LOF,OCSVM,iForest and LSTM-AE,increasing the recall rate and F1 value by at least 6%and 2.4%respectively.
作者
衡红军
刘静
HENG Hongjun;LIU Jing(School of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第3期99-104,共6页
Computer Engineering
基金
国家自然科学基金(U1333109)。
关键词
多维时间序列
异常点检测
自动编码器
孤立森林算法
特征提取
multidimensional time series
outlier detection
autoencoder
isolation forest algorithm
feature extraction
