Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
Impact in
Classified as
- Authors
- Jihun YiSungroh Yoon
- Journal
- IEEE Access
In The Last Decade
doi.org/10.1109/access.2021.3107975 →Countries where authors are citing Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
This map shows the geographic impact of Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines more than expected).
Fields of papers citing Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
This network shows the impact of Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines.
About Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
This paper, published in 2021, received 384 indexed citations . Written by Jihun Yi and Sungroh Yoon covering the research area of Artificial Intelligence, Computer Networks and Communications and Signal Processing. It is primarily cited by scholars working on Artificial Intelligence (262 citations), Signal Processing (127 citations), Computer Networks and Communications (112 citations), Control and Systems Engineering (76 citations) and Electrical and Electronic Engineering (31 citations). Published in IEEE Access.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/access.2021.3107975.