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.
@article{arxiv.1911.08793,
title = {A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks},
author = {Neema Davis and Gaurav Raina and Krishna Jagannathan},
journal= {arXiv preprint arXiv:1911.08793},
year = {2019}
}
Comments
Preprint submitted to Elsevier TRIP. arXiv admin note: text overlap with arXiv:1909.06041