A framework for end-to-end deep learning-based anomaly detection in transportation networks

Transportation Research Interdisciplinary Perspectives

Gaurav Raina¹  Neema Davis²  Krishna Jagannathan³ 

[1] Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India;Corresponding author.;Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600 036, India;

关键词: End-to-end anomaly detection;    LSTM;    Extreme value theory;   

DOI  :  

来源: DOAJ

【 摘 要 】

We develop an end-to-end deep learning-based anomaly detection model for temporal data in transportation networks. The proposed EVT-LSTM model is derived from the popular LSTM (Long Short-Term Memory) network and adopts an objective function that is based on fundamental results from EVT (Extreme Value Theory). We compare the EVT-LSTM model with some established statistical, machine learning, and hybrid deep learning baselines. Experiments on seven diverse real-world data sets demonstrate the superior anomaly detection performance of our proposed model over the other models considered in the comparison study.

【 授权许可】

Unknown