The article discusses how choosing the right components for a semiparametric time series regression model can lead to more accurate predictions. By using a novel cross-validation model selection procedure, researchers identified the most important factors for both parametric and nonparametric components. This approach helps avoid unnecessary complexity and computational challenges caused by sparse data. The study shows that the proposed selection procedure performs well in practice, improving the accuracy of predictions in both simulated and real-world examples.