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Outliers in multivariate time series
Open accessAbstract
This paper generalises four types of disturbance commonly used in univariate time series analysis to the multivariate case, highlights the differences between univariate and multivariate outliers, and investigates dynamic effects of a multivariate outlier on individual components. The effect of a multivariate outlier depends not only on its size and the underlying model, but also on the interaction between the size and the dynamic structure of the model. The latter factor does not appear in the univariate case. A multivariate outlier can introduce various types of outlier for the marginal component models. By comparing and contrasting results of univariate and multivariate outlier detections, one can gain insights into the characteristics of an outlier. We use real examples to demonstrate the proposed analysis.
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Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Analysis of Financial Time Series
(2006) · Technometrics · doi:10.1198/tech.2006.s405 · Cited by 2,348
A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102
A Unifying Review of Deep and Shallow Anomaly Detection
Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Muller, K.-R. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2021.3052449 · Cited by 914
Metadata
- Authors
- Tsay, R. S.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-12-01
- Volume
- 87
- Issue
- 4
- Pages
- 789-804
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2000145235
- Cited by
- 171
- References
- 0
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- green
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- Tsay, R. S. (2000). Outliers in multivariate time series. Biometrika. https://doi.org/10.1093/biomet/87.4.789
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