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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A joint model for IT operation series prediction and anomaly detection
Run-Qing Chen, Guanghui Shi, Wan‐Lei Zhao, et al.
Neurocomputing (2021) Vol. 448, pp. 130-139
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

Anomaly detection in time series
Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock
Proceedings of the VLDB Endowment (2022) Vol. 15, Iss. 9, pp. 1779-1797
Closed Access | Times Cited: 229
An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series
Astha Garg, Wenyu Zhang, Jules Samaran, et al.
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 33, Iss. 6, pp. 2508-2517
Open Access | Times Cited: 131
Detecting cyberattacks using anomaly detection in industrial control systems: A Federated Learning approach
Trương Thu Hương, Ta Phuong Bac, Dao Minh Long, et al.
Computers in Industry (2021) Vol. 132, pp. 103509-103509
Open Access | Times Cited: 123
Deep Learning for Time Series Anomaly Detection: A Survey
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, et al.
ACM Computing Surveys (2024) Vol. 57, Iss. 1, pp. 1-42
Open Access | Times Cited: 33
Deep Learning for Time Series Anomaly Detection: A Survey
Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 38
Explainable Anomaly Detection for Industrial Control System Cybersecurity
Do Thu Ha, Hoang Xuan Nguyen, Nguyễn Hoàng Việt, et al.
IFAC-PapersOnLine (2022) Vol. 55, Iss. 10, pp. 1183-1188
Open Access | Times Cited: 37
Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series
Sondre Sørbø, Massimiliano Ruocco
Data Mining and Knowledge Discovery (2023) Vol. 38, Iss. 3, pp. 1027-1068
Open Access | Times Cited: 16
A two-stage adversarial Transformer based approach for multivariate industrial time series anomaly detection
Junfu Chen, Dechang Pi, Xixuan Wang
Applied Intelligence (2024) Vol. 54, Iss. 5, pp. 4210-4229
Closed Access | Times Cited: 5
Artificial intelligence advances in anomaly detection for telecom networks
Enerst Edozie, Aliyu Nuhu Shuaibu, Bashir Olaniyi Sadiq, et al.
Artificial Intelligence Review (2025) Vol. 58, Iss. 4
Open Access
Enhancing autoencoder models for multivariate time series anomaly detection: the role of noise and data amount
Shahin Sefati, Seyed Naser Razavi, Pedram Salehpour
The Journal of Supercomputing (2025) Vol. 81, Iss. 4
Closed Access
Real-Time Monitoring and Anomaly Detection in Server Lab Environments Using ESP32 DevKit and VAE-Bi-LSTM Hybrid Model: A Case Study at HUFLIT University, Ho Chi Minh City
Trong Hieu Le, Long Tan Le, Tran Chi Nhan, et al.
Lecture notes in computer science (2025), pp. 127-138
Closed Access
An Unsupervised Fusion Strategy for Anomaly Detection via Chebyshev Graph Convolution and a Modified Adversarial Network
Hassan Manafi, Farnaz Mahan, Habib Izadkhah
Biomimetics (2025) Vol. 10, Iss. 4, pp. 245-245
Open Access
Efficient time series anomaly detection by multiresolution self-supervised discriminative network
Desen Huang, Lifeng Shen, Zhongzhong Yu, et al.
Neurocomputing (2022) Vol. 491, pp. 261-272
Closed Access | Times Cited: 17
Unsupervised detecting anomalies in multivariate time series by Robust Convolutional LSTM Encoder–Decoder (RCLED)
Tuan M. V. Le, Hai Canh Vu, Amélie Ponchet-Durupt, et al.
Neurocomputing (2024) Vol. 592, pp. 127791-127791
Closed Access | Times Cited: 2
Variational Autoencoders for Biomedical Signal Morphology Clustering and Noise Detection
Zhale Nowroozilarki, Bobak J. Mortazavi, Roozbeh Jafari
IEEE Journal of Biomedical and Health Informatics (2023) Vol. 28, Iss. 1, pp. 169-180
Closed Access | Times Cited: 6
Fake it till you Detect it: Continual Anomaly Detection in Multivariate Time-Series using Generative AI
Gastón García González, Pedro Casas, Alicia Fernández
(2023), pp. 558-566
Closed Access | Times Cited: 5
One Model to Find Them All Deep Learning for Multivariate Time-Series Anomaly Detection in Mobile Network Data
Gastón García González, Sergio Martinez Tagliafico, Alicia Fernández, et al.
IEEE Transactions on Network and Service Management (2023) Vol. 21, Iss. 2, pp. 1601-1616
Open Access | Times Cited: 5
Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model
Thomas J. Templin, Milad Memarzadeh, Walter Vinci, et al.
Machine Learning Science and Technology (2023) Vol. 4, Iss. 3, pp. 035018-035018
Open Access | Times Cited: 4
A novel deep learning framework with variational auto-encoder for indoor air quality prediction
Qiyue Wu, Yun Geng, Xinyuan Wang, et al.
Frontiers of Environmental Science & Engineering (2023) Vol. 18, Iss. 1
Closed Access | Times Cited: 4
Using Multi-Scale Convolution Fusion and Memory-Augmented Adversarial Autoencoder to Detect Diverse Anomalies in Multivariate Time Series
Zefei Ning, Hao Miao, Zhuolun Jiang, et al.
Tsinghua Science & Technology (2024) Vol. 30, Iss. 1, pp. 234-246
Closed Access | Times Cited: 1
An explainable unsupervised anomaly detection framework for Industrial Internet of Things
Yilixiati Abudurexiti, Guangjie Han, Fan Zhang, et al.
Computers & Security (2024), pp. 104130-104130
Closed Access | Times Cited: 1
A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
Ben Scott, Michael N. Johnstone, Patryk Szewczyk
Sensors (2024) Vol. 24, Iss. 19, pp. 6414-6414
Open Access | Times Cited: 1
DC-VAE, Fine-grained Anomaly Detection in Multivariate Time-Series with Dilated Convolutions and Variational Auto Encoders
Gastón García González, Sergio Martinez Tagliafico, Alicia Fernandez Iie-Fing, et al.
(2022), pp. 287-293
Closed Access | Times Cited: 7
Anomaly detection in rolling bearings based on the Mel‐frequency cepstrum coefficient and masked autoencoder for distribution estimation
Suchao Xie, Runda Liu, Leilei Du, et al.
Structural Control and Health Monitoring (2022) Vol. 29, Iss. 11
Open Access | Times Cited: 7
DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection
Yun Zhao, Xiuguo Zhang, Zijing Shang, et al.
Entropy (2022) Vol. 24, Iss. 11, pp. 1613-1613
Open Access | Times Cited: 6
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