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This framework, also applicable for cyclostationary and multivariate modelling, is augmented with flexible parametric correlation structures that parsimoniously describe observed correlations.

However, robust multivariate modelling is slow.

In addition, multivariate modelling is preformed in order to visualize and compare the results obtained from the different statistical approaches.

A bivariate case of the proposed multivariate model is presented.

The multivariate model is then assessed based on the Bayesian point and interval hypothesis testing approaches.

Another multivariate model is proposed by King et al.

Accordingly, their contribution in the respective multivariate models is high.

The resolved multivariate model is presented in Table 3C.

The core multivariate model is presented in Table  2.

The multivariate model is a mixed model with random intercepts and random slopes versus time [ 18].

While the multivariate model is statistically significant, its predictive power is low.