SVM kernels for time series analysis

Stefan Rüping

No 2001,43, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

Abstract: 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.

Keywords: Support Vector Machines; Time Series (search for similar items in EconPapers)
Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:200143

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