Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis

TL;DR

This paper introduces a PCA-based method to orthogonalize models in Bayesian forecast mixing, reducing redundancy and improving prediction accuracy and uncertainty quantification.

Contribution

It presents a novel approach combining PCA-based orthogonalization with Bayesian model averaging to enhance forecast reliability.

Findings

Improved prediction accuracy with orthogonalized models

Enhanced uncertainty quantification performance

Reduction of model redundancy in Bayesian forecast mixing

Abstract

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In many cases, however, the models used in the mixing process are similar. In addition to contaminating the model space, the existence of such similar, or even redundant, models during the multimodeling process can result in misinterpretation of results and deterioration of predictive performance. In this work we describe a method based on the Principal Component Analysis that eliminates model redundancy. We show that by adding model orthogonalization to the proposed Bayesian Model Combination framework, one can arrive at better prediction accuracy and reach excellent uncertainty quantification performance.