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.

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Year of publication:	2001
Authors:	Rüping, Stefan
Institutions:	Institut für Wirtschafts- und Sozialstatistik, Universität Dortmund
Subject:	Support Vector Machines \| Time Series

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Extent:	application/pdf
Series:	Technical Reports.
Type of publication:	Book / Working Paper
Notes:	Number 2001,43
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10010982403