A Comprehensive Survey on Rare Event Prediction

TL;DR

This survey comprehensively reviews current methods for rare event prediction across data types, processing, algorithms, and evaluation, highlighting gaps and future research directions in this challenging field.

Contribution

It provides an extensive overview of 73 datasets, categorizes approaches, and identifies research gaps in rare event prediction, guiding future work.

Findings

Analyzed 73 diverse datasets across modalities.

Categorized approaches into data processing, algorithms, and evaluation.

Highlighted key challenges and future research directions.

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 statistical 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.