Cyclocopula Technique to Study the Relationship Between Two Cyclostationary Time Series with Fractional Brownian Motion Errors
This addresses a specific issue in environmental and hydrological studies where existing techniques are sensitive to stationarity assumptions, but it appears incremental as it adapts copula methods to a specialized context.
The researchers tackled the problem of detecting relationships between two cyclostationary time series with fractional Brownian motion errors, introducing a new copula-based method and verifying its performance through numerical studies.
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.