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Classification of cyclical time series using complex demodulation

机译:使用复杂解调对周期性时间序列进行分类

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摘要

A new and innovative procedure based on time varying amplitudes for the classification of cyclical time series is proposed. In many practical situations, the amplitude of a cyclical component of a time series is not constant. Estimated time varying amplitudes obtained through complex demodulation of the time series are used as the discriminating variables in classical discriminant analysis. The aim of this paper is to demonstrate through simulation studies and applications to well-known data sets, that time varying amplitudes have very good discriminating power and hence their use in classical discriminant analysis is a simple alternative to more complex methods of time series discrimination.
机译:提出了一种基于时变幅度的新的创新方法,用于循环时间序列的分类。在许多实际情况下,时间序列的周期性分量的幅度不是恒定的。通过时间序列的复杂解调获得的估计时变幅度被用作经典判别分析中的判别变量。本文的目的是通过仿真研究和对知名数据集的应用证明,时变幅度具有很好的判别能力,因此,将其用于经典判别分析中是对更复杂的时间序列判别方法的一种简单替代方法。

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