2022
DOI: 10.31219/osf.io/k6ajf
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Cyclocopula Technique to Study The Relationship Between Two Cyclostationary Time Series With Fractional Brownian Motion Errors

Abstract: Detection of 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.

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Cited by 2 publications
(1 citation statement)
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“…8). For future studies, a comparative analysis of the ensemble [22,23], tree-based [24], ANFIS [25], deep learning [26], artificial neural network [27] support vector machine [28], hybrid [29,30], clustering and classification [31], and other advanced statistics methods [32,33] is proposed for an insight into an optimal model with higher accuracy. APPENDIX Consider the nonlinear system adapted from [13] as follows.…”
Section: Discussionmentioning
confidence: 99%
“…8). For future studies, a comparative analysis of the ensemble [22,23], tree-based [24], ANFIS [25], deep learning [26], artificial neural network [27] support vector machine [28], hybrid [29,30], clustering and classification [31], and other advanced statistics methods [32,33] is proposed for an insight into an optimal model with higher accuracy. APPENDIX Consider the nonlinear system adapted from [13] as follows.…”
Section: Discussionmentioning
confidence: 99%