SVM Kernels for Time Series Analysis

  • Rüping S
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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 disusses their relative merits, depending on the type of data that is used

Author-supplied keywords

  • Linear kernel
  • PHMM
  • RBF kernel
  • Subsequence kernel
  • support vector machines
  • time series

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  • S Rüping

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