Real-time Bayesian anomaly detection in streaming environmental data-Reference-Cited by-同舟云学术

Real-time Bayesian anomaly detection in streaming environmental data

Published:2009-03-04 Issue:4 Volume:45 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Hill David J.¹,Minsker Barbara S.²,Amir Eyal³

Affiliation:

1. National Center for Supercomputing Applications; University of Illinois at Urbana-Champaign; Urbana Illinois USA

2. Department of Civil and Environmental Engineering; University of Illinois at Urbana-Champaign; Urbana Illinois USA

3. Department of Computer Science; University of Illinois at Urbana-Champaign; Urbana Illinois USA

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1029/2008WR006956/fullpdf

Reference39 articles.

1. Efficient simulation of the von Mises distribution;Best;Appl. Stat.,1979

2. Blum , R. S. Y. Zhang B. Sadler R. Kozick 1999 On the approximation of correlated non-Gaussian noise pdfs using Gaussian mixture models 1st Conference on the Applications of Heavy Tailed Distributions in Economics, Engineering and Statistics Washington, D. C.

3. Rao-Blackwellisation of sampling schemes;Casella;Biometrika,1996

Cited by 46 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks;Environmental Monitoring and Assessment;2024-07-04

2. Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data;Stochastic Environmental Research and Risk Assessment;2024-07-01

3. Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models;Sensors;2024-05-01

4. EGNN: Energy-efficient anomaly detection for IoT multivariate time series data using graph neural network;Future Generation Computer Systems;2024-02

5. A Light-Weight Real-Time Anomaly Detection Framework for Edge Computing;Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23);2023