Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors

TL;DR

This paper introduces a new copula-based method to detect relationships between two cyclostationary time series with fractional Brownian motion errors, addressing limitations of existing techniques sensitive to stationarity assumptions.

Contribution

The paper presents a novel copula-based approach specifically designed for cyclostationary time series with fBm errors, improving relationship detection accuracy.

Findings

Numerical studies verify the effectiveness of the proposed method.

The approach outperforms traditional techniques sensitive to stationarity.

The method is applicable to environmental and hydrological data analysis.

Abstract

Detection of the relationship between two time series is so important in environmental and hydrological studies. Several parametric and non-parametric approaches can be applied to detect relationships. These techniques are usually sensitive to stationarity assumptions. In this research, a new copula-based method is introduced to detect the relationship between two cylostationary time series with fractional Brownian motion (fBm) errors. The numerical studies verify the performance of the introduced approach.