Shapley value-driven superior subset selection algorithm for carbon price interval forecast combination.

ABSTRACT: Interval prediction requires not only accuracy but also the consideration of interval width and coverage, making model selection complex. However, research rarely addresses this challenge in interval combination forecasting. To address this issue, this study introduces a model selection for interval forecast combination based on the Shapley value (MSIFC-SV). This algorithm calculates Shapley values to measure each model's marginal contribution and establishes a redundancy criterion on the basis of changes in interval scores. If the removal of a model does not decrease the interval score, it is considered redundant and excluded. The selection process starts with all the models and ranks them by their Shapley values. Models are then assessed for retention or removal according to the redundan

SUBMITTER: Yang J 

PROVIDER: S-EPMC11868652 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature