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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >Comparison and classification of stationary multivariate time series
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Comparison and classification of stationary multivariate time series

机译:平稳多元时间序列的比较和分类

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摘要

This paper presents procedures to compare and classify stationary multivariate time series. The classification procedure is based on the p-value of a test of hypothesis that is performed for every pair of series under consideration. The test of hypothesis is based on the difference between vector autoregressive parameter estimates of the series. Simulation studies show that the test of hypothesis and the classification procedure perform fairly well for series of reasonable length.
机译:本文提出了比较和分类平稳多元时间序列的程序。分类过程基于对假设的每对序列执行的假设检验的p值。假设检验基于序列的向量自回归参数估计之间的差异。仿真研究表明,对于合理长度的序列,假设检验和分类程序的性能都很好。

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