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Robust tests for time series comparison based on Laplace periodograms

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  • Jin, Lei

Abstract

Statistical comparison of time series is useful for the detection of mechanical damage and many other real-world applications. New methods have been proposed to check whether two semi-stationary time series have the same normalized dynamics. The proposed methods differ from traditional methods in that they are based on the Laplace periodogram, which is a robust tool to analyze the serial dependence of time series. Via the method of estimating equations, a generalized score statistic and an order selected statistic are developed for the comparison. Their asymptotic distributions under the null are obtained. The proposed methods are applicable to compare two semi-stationary time series which may be dependent on each other. They also can be used to compare two time series whose traditional spectral densities or autocovariance structures may not exist. A Monte Carlo simulation study illustrates the validity of the asymptotic results and the finite sample performance. The proposed methods have been applied to an analysis of non-stationary vibration signals for mechanical damage detection.

Suggested Citation

  • Jin, Lei, 2021. "Robust tests for time series comparison based on Laplace periodograms," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:csdana:v:160:y:2021:i:c:s0167947321000578
    DOI: 10.1016/j.csda.2021.107223
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    References listed on IDEAS

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