Beyond Accuracy: Building Trustworthy Extreme Events Predictions Through Explainable Machine Learning

Abstract

Extreme events, despite their rarity, pose a significant threat due to their immense impact. While machine learning has emerged as a game-changer for predicting these events, the crucial challenge lies in trusting these predictions. Existing studies primarily focus on improving accuracy, neglecting the crucial aspect of model explainability. This gap hinders the integration of these solutions into decision-making processes. Addressing this critical issue, this paper investigates the explainability of extreme event forecasting using a hybrid forecasting and classification approach. By focusing on two economic indicators, Business Confidence Index (BCI) and Consumer Confidence Index (CCI), the study aims to understand why and when extreme event predictions can be trusted, especially in the context of imbalanced classes (normal vs. extreme events). Machine learning models are comparatively analysed, exploring their explainability through dedicated tools. Additionally, various class balancing methods are assessed for their effectiveness. This combined approach delves into the factors influencing extreme event prediction accuracy, offering valuable insights for building trustworthy forecasting models.