Improved LSTM-Based Time-Series Anomaly Detection in Rail Transit Operation Environments-Reference-Cited by-同舟云学术

Improved LSTM-Based Time-Series Anomaly Detection in Rail Transit Operation Environments

Published:2022-12 Issue:12 Volume:18 Page:9027-9036
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Wang Yujie¹^ORCID,Du Xin¹^ORCID,Lu Zhihui¹^ORCID,Duan Qiang²^ORCID,Wu Jie¹

Affiliation:

1. School of Computer Science, Fudan University, Shanghai, China

2. School of Information Sciences and Technology, Pennsylvania State University, Abington, PA, USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Shanghai Science and Technology Innovation Action Plan Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/9906876/09748023.pdf?arnumber=9748023

Reference25 articles.

1. Deep learning for anomaly detection;chalapathy;CoRR,2019

2. Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data;shipmon;CoRR,2017

3. Outlier Detection Using Replicator Neural Networks

4. Graph Based Approach to Real-Time Metro Passenger Flow Anomaly Detection

5. A Semi-Supervised Railway Foreign Object Detection Method Based on GAN

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