[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122884-en":3,"doc-seo-122884-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122884,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Beyond Accuracy - Building Trustworthy Extreme Events Predictions Through Explainable Machine Learning","Extreme events, despite their rarity, can cause disproportionate damage, making reliable forecasting essential. While machine learning improves predictive performance, users still face a core barrier: trusting model outputs when classes are imbalanced and explanations are limited. This paper studies explainability in extreme event forecasting via a hybrid forecasting-and-classification approach on two economic indicators—Business Confidence Index (BCI) and Consumer Confidence Index (CCI). Machine learning models are compared using dedicated explainability tools, and class-balancing strategies are evaluated to identify drivers of trustworthy predictions.","Beyond Accuracy: Building Trustworthy Extreme Events Predictions Through Explainable Machine Learning  \nChristian Mulomba Mukendi 􀀍  \nDoctoral Student, Department ofAdvanced Convergence, Handong Global University, Pohang, South-Korea  \nAsser Kasai Itakala   \nMaster of Science, Department of Global Development and Entrepreneurship, Handong Global University, Pohang, South-Korea  \nPierrot Muteba Tibasima   \nMaster Student, Department of Global Development and Entrepreneurship, Handong Global University, Pohang, South-Korea  \n\n| Suggested Citation |\n| --- |\n| Mukendi, C.M., Itakala, A.K. & Tibasima, P.M. (2024) . Beyond Accuracy: Building Trustworthy Extreme Events Predictions Through Explainable Machine Learning. European Journal of Theoretical and Applied Sciences, 2(1), 199-218.\u003Cbr>DOI: 10.59324/ejtas.2024.2(1).15 |\n\nAbstract:  \nExtreme 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  \nConsumer 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.  \nKeywords: Extreme events prediction, explainable machine learning, class imbalance.  \nIntroduction  \nExtreme events have attracted huge attention of researchers last few decades considering their two-edged threat: small number but large in impact which poses a particularly difficult quandary (Chen, Gupta, and Tragoudas 2022; Ghil et al. 2011) . Studies on the topic cover their summarization, detection, and prediction indifferent areas such as finance, weather, etc  \n(Zhao 2020) . On the aspect of their prediction, with the large amount of data generated day-today, machine learning (deep learning) is seen asa game changer due to its ability to capture hidden patterns in data to generate accurate predictions, which represents the major limitation of the statistical approach. Thus, powerful frameworks have been developed to provide better results in this scope of studies. However, techniques applied in forecasting of  \nExtreme Events are generally in their infancy and mostly domain specific and despite the large advances achieved in their forecasting resulting from the use of machine learning techniques, the crucial difficulty lies not so much in finding new prediction methods but in finding ways to trust these predictions. The available literature considers mostly the improvement in forecasting accuracy such as in (Ding et al. 2019) which propose improvement in forecasting of extreme events considering the extreme value loss instead of the quadratic loss. The analysis and prediction of the outbreak of corona virus is proposed in (Petropoulos and Makridakis 2020) where machine learning was able to unveil hidden valuable information to detect future outbreak of the pandemic. Due to the imbalance classes in the prediction of extreme events, a technique based on block resampling in joint predictor forecast space is proposed in (Chen et al. 2022) . While these studies propose various outstanding approach to impr","cbCaikeAsFxNyxNo","https://ap.wps.com/l/cbCaikeAsFxNyxNo","pdf",2052082,1,20,"English","en",105,"# Introduction\n## Problem background and motivation\n## Research gap: accuracy vs. explainability\n# Methodology\n## Hybrid forecasting and classification approach\n## Explainability tools and model comparison\n## Class balancing strategies\n# Results and Discussion\n## Interpretation of trusted extreme-event predictions\n# Conclusion","[{\"question\":\"Why is explainability important for extreme event predictions?\",\"answer\":\"Because relying only on improved accuracy does not ensure that decision-makers can trust the model outputs, especially as model complexity increases.\"},{\"question\":\"What indicators are used to build the extreme-event forecasting case study?\",\"answer\":\"The Business Confidence Index (BCI) and the Consumer Confidence Index (CCI) are used as two economic indicators for predicting joint trends.\"},{\"question\":\"How does the paper define an extreme event versus a normal event?\",\"answer\":\"A pre-defined threshold is used: values below the threshold represent an extreme event, while values above it indicate a normal event.\"}]","Beyond Accuracy - 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