Document Type
Article
Abstract
Rare event prediction involves identifying and forecasting events with a low probability using machine learning (ML) and data analysis. Due to the imbalanced data distributions, where the frequency of common events vastly outweighs that of rare events, it requires using specialized methods within each step of the ML pipeline, i.e., from data processing to algorithms to evaluation protocols. Predicting the occurrences of rare events is important for real-world applications, such as Industry 4.0, and is an active research area in statistics and ML. This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches. Specifically, we consider 73 datasets from different modalities (i.e., numerical, image, text, and audio), four major categories of data processing, five major algorithmic groupings, and two broader evaluation approaches. This paper aims to identify gaps in the current literature and highlight the challenges of predicting rare events. It also suggests potential research directions, which can help guide practitioners and researchers.
Digital Object Identifier (DOI)
Publication Info
ACM Computing Surveys, 2024.
APA Citation
Shyalika, C., Wickramarachchi, R., & Sheth, A. P. (2024). A Comprehensive Survey on Rare Event Prediction. ACM Computing Surveys. https://dl.acm.org/doi/10.1145/3699955
Rights
© Chathurangi Shyalika Jayakody Kankanamalage, Ruwan Wickramarachchi, & Amit Sheth | ACM. 2024. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Computing Surveys, https://dl.acm.org/doi/10.1145/3699955.