A computational method to compare spectral densities of independent periodically correlated time series. Issue 8 (18th April 2021) - He, Zongda; Ma, Zhonglian
Abstract: Time series comparison is an important research topic with applications mainly in fields like economics, finance, geology, marketing, medicine, physics, signal processing, among many others, when we want to know if two or more time series have the same stochastic mechanism. The comparison, classification and clustering of two or several time series models have been considered in both time and frequency domain approaches by means of many statisticians. Most of these techniques can be applied for the stationary time series. This paper deals with the problem of testing equality among spectral densities of several independent periodically correlated processes. The asymptotic distribution for the discrete Fourier transform of periodically correlated time is applied to test the equality of several independent periodically correlated time series.