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In many scientific fields such as meteorology, climatology and hydrology, one of major problems is to measure the relationships between some variables. To this aim, numerous parametric or non-parametric techniques have been introduced. But the applications of these approaches are usually restricted for some cases, for example, normal or stationary datasets. In this work, two periodically correlated time series with Mandelbrot-Van Ness fractional Brownian motion errors are considered and a novel technique to measure the relationship between them is introduced. The ability and power of the proposed method is evaluated using a deep simulation study. Moreover, the proposed procedure is used in a real world problem.

journal of statistical computation and simulation

Evaluating the relationship between two periodically correlated processes with Mandelbrot-Van Ness fractional Brownian motion errors using periodic copula