The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Outlier Detection for Temporal Data: A Survey
Abstract
In the statistics community, outlier detection for time series data has been studied for decades. Recently, with advances in hardware and software technology, there has been a large body of work on temporal outlier detection from a computational perspective within the computer science community. In particular, advances in hardware technology have enabled the availability of various forms of temporal data collection mechanisms, and advances in software technology have enabled a variety of data management mechanisms. This has fueled the growth of different kinds of data sets such as data streams, spatio-temporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications. There arises a need for an organized and detailed study of the work done in the area of outlier detection with respect to such temporal datasets. In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which specific definitions and techniques have been widely used.
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Cites
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Deep Learning for Anomaly Detection: A Review
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Outlier Detection: Methods, Models, and Classification
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“1 Related Work Compared to the abundance of taxonomies of anomaly detection methods, including but not limited to these surveys throughout the years [3, 36, 37, 78, 86, 161], the categorization of anomaly explanation methods involving XAD techniques has received relatively little attention so far [163, 186, 190, 217,…”
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Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
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IoT data cleaning techniques: A survey
Ding, X., Wang, H., Li, G., Li, H., Li, Y., & Liu, Y. (2022) · Intelligent and Converged Networks · doi:10.23919/icn.2022.0026 · Cited by 35
Explainable outlier detection: What, for Whom and Why?
Sejr, J. H., & Schneider-Kamp, A. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100172 · Cited by 32
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Blind spot detection for safe sim-to-real transfer
Ramakrishnan, R., Kamar, E., Dey, D., Horvitz, E., & Shah, J. (2020) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.11436 · Cited by 21
Online Learning of Oil Leak Anomalies in Wind Turbines with Block-Based Binary Reservoir
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MADDC: Multi-Scale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs
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Modelling the Outlier Detection Problem in ASP(Q)
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Metadata
- Authors
- Gupta, M., Gao, J., Aggarwal, C. C., & Han, J.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2014-09
- Volume
- 26
- Issue
- 9
- Pages
- 2250-2267
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2026493302
- Cited by
- 1,001
- References
- 5
- FWCI
- 56.45
- Type
- journal article
- Language
- en
- Open access
- closed
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