Arbeitspapier
SVM kernels for time series analysis
Time series analysis is an important and complex problem in machine learning and statistics. Real-world applications can consist of very large and high dimensional time series data. Support Vector Machines (SVMs) are a popular tool for the analysis of such data sets. This paper presents some SVM kernel functions and discusses their relative merits, depending on the type of data that is used.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2001,43
- Subject
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Support Vector Machines
Time Series
- Event
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Geistige Schöpfung
- (who)
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Rüping, Stefan
- Event
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Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
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Dortmund
- (when)
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2001
- Handle
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Rüping, Stefan
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2001
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Using kernels to detect abrupt changes in time series
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Nonlinear time series analysis
Multiple time series analysis
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