Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 Review on Outlier/Anomaly Detection in Time Series Data
Ane Blázquez-García, Ángel Conde, Usue Mori, et al.
ACM Computing Surveys (2021) Vol. 54, Iss. 3, pp. 1-33
Open Access | Times Cited: 553

Showing 1-25 of 553 citing articles:

Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Ailin Deng, Bryan Hooi
Proceedings of the AAAI Conference on Artificial Intelligence (2021) Vol. 35, Iss. 5, pp. 4027-4035
Open Access | Times Cited: 663
Transformers in Time Series: A Survey
Qingsong Wen, Tian Zhou, Chaoli Zhang, et al.
(2023), pp. 6778-6786
Open Access | Times Cited: 490
Learning Graph Structures With Transformer for Multivariate Time-Series Anomaly Detection in IoT
Zekai Chen, Dingshuo Chen, Xiao Zhang, et al.
IEEE Internet of Things Journal (2021) Vol. 9, Iss. 12, pp. 9179-9189
Open Access | Times Cited: 321
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
Kukjin Choi, Jihun Yi, Changhwa Park, et al.
IEEE Access (2021) Vol. 9, pp. 120043-120065
Open Access | Times Cited: 310
IoT anomaly detection methods and applications: A survey
Ayan Chatterjee, Bestoun S. Ahmed
Internet of Things (2022) Vol. 19, pp. 100568-100568
Open Access | Times Cited: 150
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: 130
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Ming Jin, Huan Yee Koh, Qingsong Wen, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2024) Vol. 46, Iss. 12, pp. 10466-10485
Open Access | Times Cited: 73
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Yiyuan Yang, Chaoli Zhang, Tian Zhou, et al.
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2023), pp. 3033-3045
Open Access | Times Cited: 55
TRNN: An efficient time-series recurrent neural network for stock price prediction
Minrong Lu, XU Xue-rong
Information Sciences (2023) Vol. 657, pp. 119951-119951
Closed Access | Times Cited: 51
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
Kexin Zhang, Qingsong Wen, Chaoli Zhang, et al.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2024) Vol. 46, Iss. 10, pp. 6775-6794
Open Access | Times Cited: 48
Image encoding selection based on Pearson correlation coefficient for time series anomaly detection
Helmy Rahadian, Steven Bandong, Augie Widyotriatmo, et al.
Alexandria Engineering Journal (2023) Vol. 82, pp. 304-322
Open Access | Times Cited: 46
CPS-GUARD: Intrusion detection for cyber-physical systems and IoT devices using outlier-aware deep autoencoders
Marta Catillo, Antonio Pecchia, Umberto Villano
Computers & Security (2023) Vol. 129, pp. 103210-103210
Open Access | Times Cited: 45
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
Yang Liu, Dingkang Yang, Yan Wang, et al.
ACM Computing Surveys (2024) Vol. 56, Iss. 7, pp. 1-38
Open Access | Times Cited: 33
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
MS2OD: outlier detection using minimum spanning tree and medoid selection
Jia Li, Jiangwei Li, Chenxu Wang, et al.
Machine Learning Science and Technology (2024) Vol. 5, Iss. 1, pp. 015025-015025
Open Access | Times Cited: 23
A Review of Time-Series Anomaly Detection Techniques: A Step to Future Perspectives
Kamran Shaukat, Talha Mahboob Alam, Suhuai Luo, et al.
Advances in intelligent systems and computing (2021), pp. 865-877
Closed Access | Times Cited: 79
TSB-UAD
John Paparrizos, Yuhao Kang, Paul Boniol, et al.
Proceedings of the VLDB Endowment (2022) Vol. 15, Iss. 8, pp. 1697-1711
Closed Access | Times Cited: 65
Performance evaluation of outlier detection techniques in production timeseries: A systematic review and meta-analysis
Hamzeh Alimohammadi, Shengnan Nancy Chen
Expert Systems with Applications (2021) Vol. 191, pp. 116371-116371
Closed Access | Times Cited: 64
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress
Renjie Wu, Eamonn Keogh
IEEE Transactions on Knowledge and Data Engineering (2021), pp. 1-1
Open Access | Times Cited: 58
Anomaly Detection Using a Sliding Window Technique and Data Imputation with Machine Learning for Hydrological Time Series
Lattawit Kulanuwat, Chantana Chantrapornchai, Montri Maleewong, et al.
Water (2021) Vol. 13, Iss. 13, pp. 1862-1862
Open Access | Times Cited: 57
Short-Term Electricity Prices Forecasting Using Functional Time Series Analysis
Faheem Jan, Ismail Shah, Sajid Ali
Energies (2022) Vol. 15, Iss. 9, pp. 3423-3423
Open Access | Times Cited: 53
Volume under the surface
John Paparrizos, Paul Boniol, Themis Palpanas, et al.
Proceedings of the VLDB Endowment (2022) Vol. 15, Iss. 11, pp. 2774-2787
Closed Access | Times Cited: 46
TFAD
Chaoli Zhang, Tian Zhou, Qingsong Wen, et al.
Proceedings of the 31st ACM International Conference on Information & Knowledge Management (2022), pp. 2497-2507
Open Access | Times Cited: 45
Review of automated time series forecasting pipelines
Stefan Meisenbacher, Marian Turowski, Kaleb Phipps, et al.
Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery (2022) Vol. 12, Iss. 6
Open Access | Times Cited: 44
A Large Comparison of Normalization Methods on Time Series
Felipe Tomazelli Lima, Vinícius M. A. Souza
Big Data Research (2023) Vol. 34, pp. 100407-100407
Closed Access | Times Cited: 36
Page 1 - Next Page

Scroll to top