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文章基本信息

  • 标题:On Selection of Periodic Kernels Parameters in Time Series Prediction
  • 本地全文:下载
  • 作者:Marcin Michalak
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2014
  • 卷号:4
  • 期号:1
  • 页码:01-10
  • DOI:10.5121/csit.2014.4101
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:In the paper the analysis of the periodic kernels parameters is described. Periodic kernels canbe used for the prediction task, performed as the typical regression problem. On the basis of thePeriodic Kernel Estimator (PerKE) the prediction of real time series is performed. As periodickernels require the setting of their parameters it is necessary to analyse their influence on theprediction quality. This paper describes an easy methodology of finding values of parameters ofperiodic kernels. It is based on grid search. Two different error measures are taken intoconsideration as the prediction qualities but lead to comparable results. The methodology wastested on benchmark and real datasets and proved to give satisfactory results.
  • 关键词:Kernel regression; time series prediction; nonparametric regression
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