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A Comprehensive Survey on Rare Event Prediction

Authors:

Chathurangi Shyalika,

Ruwan Wickramarachchi,

Amit P. ShethAuthors Info & Claims

ACM Computing Surveys, Volume 57, Issue 3

Article No.: 70, Pages 1 - 39

https://doi.org/10.1145/3699955

Published: 11 November 2024 Publication History

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, that is, 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 statistical and ML. This article 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 article 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.

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Cited By

Sudarshan VSeider W(2025)Advancing machine learning in Industry 4.0: Benchmark framework for rare-event prediction in chemical processesComputers & Chemical Engineering10.1016/j.compchemeng.2024.108929194(108929)Online publication date: Mar-2025
https://doi.org/10.1016/j.compchemeng.2024.108929
Au S(2024)A limit formula and recursive algorithm for multivariate Normal tail probabilityStatistics and Computing10.1007/s11222-024-10552-z35:1Online publication date: 28-Dec-2024
https://doi.org/10.1007/s11222-024-10552-z

Index Terms

A Comprehensive Survey on Rare Event Prediction
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 57, Issue 3

March 2025

984 pages

EISSN:1557-7341

DOI:10.1145/3697147

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
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Michela Milano
University of Bologna, Italy

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Publication History

Published: 11 November 2024

Online AM: 14 October 2024

Accepted: 01 October 2024

Revised: 16 September 2024

Received: 12 September 2023

Published in CSUR Volume 57, Issue 3

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Sudarshan VSeider W(2025)Advancing machine learning in Industry 4.0: Benchmark framework for rare-event prediction in chemical processesComputers & Chemical Engineering10.1016/j.compchemeng.2024.108929194(108929)Online publication date: Mar-2025
https://doi.org/10.1016/j.compchemeng.2024.108929
Au S(2024)A limit formula and recursive algorithm for multivariate Normal tail probabilityStatistics and Computing10.1007/s11222-024-10552-z35:1Online publication date: 28-Dec-2024
https://doi.org/10.1007/s11222-024-10552-z

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