A Review of Outlier Detection and Robust Estimation Methods for High Dimensional Time Series Data Article Swipe
Daniel Peña
,
Vı́ctor J. Yohai
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecosta.2023.02.001
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.ecosta.2023.02.001
Diagnostic procedures for finding outliers in high dimensional multivariate time series and robust estimation methods for these data are reviewed. First, methods for searching for outliers assuming that the data have been generated by a Dynamic Factor Model are presented. Then, other existing methods for detecting different types of multivariate time series outliers are analyzed. They include identifying outlying series and, also, looking for segments, or periods of time, where the series have unusual dynamics. Second, robust estimation methods are considered. Dynamic Principal Components, as a very general procedure to estimate the dynamic in a high dimensional data set, is introduced and different types of robust estimation of these components are reviewed. Dynamic Principal Components can be applied for robust estimation of Generalized Dynamic Factor models and some results are given. Finally, other methods proposed for robust estimation of high dimensional VAR models and other multivariate time series problems are also briefly discussed.
Related Topics
Concepts
Outlier
Multivariate statistics
Series (stratigraphy)
Principal component analysis
Computer science
Anomaly detection
Time series
Data mining
Robust statistics
Dynamic factor
Estimation
Pattern recognition (psychology)
Statistics
Mathematics
Artificial intelligence
Machine learning
Engineering
Biology
Systems engineering
Paleontology
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ecosta.2023.02.001
- OA Status
- hybrid
- Cited By
- 13
- References
- 90
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4319264512
All OpenAlex metadata
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https://openalex.org/W4319264512Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.ecosta.2023.02.001Digital Object Identifier
- Title
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A Review of Outlier Detection and Robust Estimation Methods for High Dimensional Time Series DataWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-01Full publication date if available
- Authors
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Daniel Peña, Vı́ctor J. YohaiList of authors in order
- Landing page
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https://doi.org/10.1016/j.ecosta.2023.02.001Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.ecosta.2023.02.001Direct OA link when available
- Concepts
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Outlier, Multivariate statistics, Series (stratigraphy), Principal component analysis, Computer science, Anomaly detection, Time series, Data mining, Robust statistics, Dynamic factor, Estimation, Pattern recognition (psychology), Statistics, Mathematics, Artificial intelligence, Machine learning, Engineering, Biology, Systems engineering, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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90Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| corresponding_author_ids | https://openalex.org/A5088521043 |
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