# Event Prediction in the Big Data Era: A Systematic Survey

arXiv: https://arxiv.org/abs/2007.09815 · HTML: https://mlsanity.com/paper/2007.09815/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 12 of 12)

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- [An Invertible Graph Diffusion Neural Network for Source Localization](https://mlsanity.com/paper/2206.09214/) (2022)
- [Modeling Events and Interactions through Temporal Processes -- A Survey](https://mlsanity.com/paper/2303.06067/) (2023)
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- [Deep Spatial Domain Generalization](https://mlsanity.com/paper/2210.00729/) (2022)
- [Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon](https://mlsanity.com/paper/2408.04377/) (2024)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [SoftED: Metrics for Soft Evaluation of Time Series Event Detection](https://mlsanity.com/paper/2304.00439/) (2023)
- [Event Prediction using Case-Based Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2309.12423/) (2023)
- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [Bridging History with AI A Comparative Evaluation of GPT 3.5, GPT4, and GoogleBARD in Predictive Accuracy and Fact Checking](https://mlsanity.com/paper/2305.07868/) (2023)
- [A Novel Method for News Article Event-Based Embedding](https://mlsanity.com/paper/2405.13071/) (2024)

## Abstract (arXiv)

Events are occurrences in specific locations, time, and semantics that nontrivially impact either our society or the nature, such as civil unrest, system failures, and epidemics. It is highly desirable to be able to anticipate the occurrence of such events in advance in order to reduce the potential social upheaval and damage caused. Event prediction, which has traditionally been prohibitively challenging, is now becoming a viable option in the big data era and is thus experiencing rapid growth. There is a large amount of existing work that focuses on addressing the challenges involved, including heterogeneous multi-faceted outputs, complex dependencies, and streaming data feeds. Most existing event prediction methods were initially designed to deal with specific application domains, though the techniques and evaluation procedures utilized are usually generalizable across different domains. However, it is imperative yet difficult to cross-reference the techniques across different domains, given the absence of a comprehensive literature survey for event prediction. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction in the big data era. First, systematic categorization and summary of existing techniques are presented, which facilitate domain experts' searches for suitable techniques and help model developers consolidate their research at the frontiers. Then, comprehensive categorization and summary of major application domains are provided. Evaluation metrics and procedures are summarized and standardized to unify the understanding of model performance among stakeholders, model developers, and domain experts in various application domains. Finally, open problems and future directions for this promising and important domain are elucidated and discussed.