# A Comprehensive Survey on Rare Event Prediction

arXiv: https://arxiv.org/abs/2309.11356 · HTML: https://mlsanity.com/paper/2309.11356/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 232 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators](https://mlsanity.com/paper/1910.03193/) (2019)
- ["Curse of rarity" for autonomous vehicles](https://mlsanity.com/paper/2207.02749/) (2022)
- [Discovering and forecasting extreme events via active learning in neural operators](https://mlsanity.com/paper/2204.02488/) (2022)
- [A Systematic Study of Online Class Imbalance Learning with Concept Drift](https://mlsanity.com/paper/1703.06683/) (2017)
- [Batch Stationary Distribution Estimation](https://mlsanity.com/paper/2003.00722/) (2020)
- [Few-shot acoustic event detection via meta-learning](https://mlsanity.com/paper/2002.09143/) (2020)
- [Rare Event Detection using Disentangled Representation Learning](https://mlsanity.com/paper/1812.01285/) (2018)
- [Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction](https://mlsanity.com/paper/2303.12534/) (2023)
- [Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation](https://mlsanity.com/paper/2106.13790/) (2021)
- [Dataset: Rare Event Classification in Multivariate Time Series](https://mlsanity.com/paper/1809.10717/) (2018)
- [Rare-Event Simulation for Neural Network and Random Forest Predictors](https://mlsanity.com/paper/2010.04890/) (2020)
- [Soft Attention Convolutional Neural Networks for Rare Event Detection in Sequences](https://mlsanity.com/paper/2011.02338/) (2020)
- [Firth's logistic regression with rare events: accurate effect estimates AND predictions?](https://mlsanity.com/paper/2101.07620/) (2021)
- [Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features](https://mlsanity.com/paper/2104.10519/) (2021)

## Built on by (most cited first, 18 of 18)

- [CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution](https://mlsanity.com/paper/2604.15840/) (2026)
- [NSF-MAP: Neurosymbolic Multimodal Fusion for Robust and Interpretable Anomaly Prediction in Assembly Pipelines](https://mlsanity.com/paper/2505.06333/) (2025)
- [Synthesizing Attitudes, Predicting Actions (SAPA): Behavioral Theory-Guided LLMs for Ridesourcing Mode Choice Modeling](https://mlsanity.com/paper/2509.18181/) (2025)
- [Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing](https://mlsanity.com/paper/2407.01644/) (2024)
- [DetectiumFire: A Comprehensive Multi-modal Dataset Bridging Vision and Language for Fire Understanding](https://mlsanity.com/paper/2511.02495/) (2025)
- [Gradient Boosting Reinforcement Learning](https://mlsanity.com/paper/2407.08250/) (2024)
- [Advancing Machine Learning in Industry 4.0: Benchmark Framework for Rare-event Prediction in Chemical Processes](https://mlsanity.com/paper/2409.00485/) (2024)
- [Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability](https://mlsanity.com/paper/2511.04474/) (2025)
- [Generative Augmentation of Imbalanced Flight Records for Flight Diversion Prediction: A Multi-objective Optimisation Framework](https://mlsanity.com/paper/2604.20288/) (2026)
- [Quantum enhanced rare event discovery and sampling](https://mlsanity.com/paper/2606.06316/) (2026)
- [Rare Event Detection in Imbalanced Multi-Class Datasets Using an Optimal MIP-Based Ensemble Weighting Approach](https://mlsanity.com/paper/2412.13439/) (2024)
- [SYNAPSE-G: Bridging Large Language Models and Graph Learning for Rare Event Classification](https://mlsanity.com/paper/2508.09544/) (2025)
- [Critical appraisal of artificial intelligence for rare-event recognition: principles and pharmacovigilance case studies](https://mlsanity.com/paper/2510.04341/) (2025)
- [xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion](https://mlsanity.com/paper/2510.20651/) (2025)
- [Rare Event Early Detection: A Dataset of Sepsis Onset for Critically Ill Trauma Patients](https://mlsanity.com/paper/2602.02930/) (2026)
- [Hard to See, Hard to Label: Generative and Symbolic Acquisition for Subtle Visual Phenomena](https://mlsanity.com/paper/2604.22990/) (2026)
- [L-FNO: Lorentzian Fourier Neural Operator for Stochastic Event Dynamics](https://mlsanity.com/paper/2608.13562/) (2026)
- [AI-based detection of worsening heart failure from low-resolution telemonitoring data](https://mlsanity.com/paper/2609.29742/) (2026)

## Abstract (arXiv)

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.