The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Outliers, level shifts, and variance changes in time series
Abstract
Outliers, level shifts, and variance changes are commonplace in applied time series analysis. However, their existence is often ignored and their impact is overlooked, for the lack of simple and useful methods to detect and handle those extraordinary events. The problem of detecting outliers, level shifts, and variance changes in a univariate time series is considered. The methods employed are extremely simple yet useful. Only the least squares techniques and residual variance ratios are used. The effectiveness of these simple methods is demonstrated by analysing three real data sets.
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Time Series Model Specification in the Presence of Outliers
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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
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Metadata
- Authors
- Tsay, R. S.
- Venue
- Journal of Forecasting
- Publisher
- Wiley
- Published
- 1988-01
- Volume
- 7
- Issue
- 1
- Pages
- 1-20
- ISSN
- 0277-6693 (ISSN-L)
- 1099-131X
- OpenAlex
- W2125793385
- Cited by
- 722
- References
- 20
- FWCI
- 14.91
- Type
- journal article
- Language
- en
- Open access
- closed
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