A random forest approach for interval selection in functional regression

In this article, we focus on the problem of variable selection in a functional regression framework. This question is motivated by practical applications in the field of agronomy: In this field, identifying the temporal periods during which weather measurements have the greatest impact on yield is critical for guiding agriculture practices in a changing environment. From a methodological point of view, our goal is to identify consecutive measurement points in the definition domain of the functional predictors, which correspond to the most important intervals for the prediction of a numeric output from the functional variables. We propose an approach based on the versatile random forest method that benefits from its good performances for variable selection and prediction. Our method builds in three steps (interval creation, summary, and selection). Different variants for each of the steps are proposed and compared on both simulated and real‐life datasets. The performances of our method compared to alternative approaches highlight its usefulness to select relevant intervals while maintaining good prediction capabilities. All variants of our method are available in the R package SISIR.