计算机工程 ›› 2020, Vol. 46 ›› Issue (3): 99-104.doi: 10.19678/j.issn.1000-3428.0055186

• 人工智能与模式识别 • 上一篇    下一篇

基于混合方法的多维时间序列驾驶异常点检测

衡红军, 刘静   

  1. 中国民航大学 计算机科学与技术学院, 天津 300300
  • 收稿日期:2019-06-12 修回日期:2019-07-18 发布日期:2019-08-06
  • 作者简介:衡红军(1968-),男,副教授、博士,主研方向为智能信息处理;刘静,硕士研究生。
  • 基金项目:
    国家自然科学基金(U1333109)。

Driving Outlier Detection Using Multidimensional Time Series Based on Hybrid Methods

HENG Hongjun, LIU Jing   

  1. School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China
  • Received:2019-06-12 Revised:2019-07-18 Published:2019-08-06

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。

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

Abstract: 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.

Key words: multidimensional time series, outlier detection, autoencoder, isolation forest algorithm, feature extraction

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