The article discusses a method to estimate time series models that separate temporal patterns from overall behavior. By using copula functions, the researchers can analyze how processes change over time independently of their individual characteristics. They found that their approach allows for accurate estimation of important features like conditional moments and quantiles in transition distributions. This method is useful for predicting risks in portfolios and other financial applications.