Driving Outlier Detection Using Multidimensional Time Series Based on Hybrid Methods
摘要
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.