modelimportance: An R package for evaluating model importance within a multi-model ensemble

2026-05-28

AI Summary

Quantifying the contribution of individual submodels to the overall predictive performance of ensemble systems is crucial for enhancing interpretability and construction efficiency. This work proposes and implements a unified R package that, for the first time in the R ecosystem, systematically supports model importance assessment across diverse ensemble methods under both point and probabilistic forecasting frameworks, with full compatibility with the hubverse infrastructure. The package offers flexible importance metrics and robust handling of missing values, substantially improving the understanding of submodel roles. It thereby empowers researchers to efficiently construct, diagnose, and optimize ensemble forecasting systems.

Abstract

Ensemble forecasts are commonly used to support decision-making and policy planning across various fields because they often offer improved accuracy and stability compared to individual models. As each model has its own unique characteristics, understanding and measuring the value of each constituent model can support the construction of effective ensembles. The R package modelimportance provides tools to quantify how each component model contributes to the accuracy of ensemble performance for both point and probabilistic forecasts. The package supports multiple ensemble methods and multiple model importance metrics. Additionally, the software offers customizable options for handling missing values. These features enable the package to serve as a versatile tool for researchers and practitioners. It helps not only in constructing an effective ensemble model across a wide range of forecasting tasks, but also in understanding the role of each model within the ensemble and gaining insights into individual models themselves. This package follows the 'hubverse' framework, which is a collection of open-source software, tools and data standards developed to promote collaborative modeling hub efforts and simplify their setup and operation. Doing so enables seamless integration and flexibility with other forecasting tools and systems, allowing many analyses to be performed on existing hubs.

Problem

• model importance
• ensemble forecasting
• multi-model ensemble
• forecast accuracy
• probabilistic forecasts

Innovation

• model importance
• ensemble forecasting
• R package
• hubverse
• probabilistic forecasts