# A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas

arXiv: https://arxiv.org/abs/2302.04018 · HTML: https://mlsanity.com/paper/2302.04018/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 65 of its 387 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 30 of 65)

- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next](https://mlsanity.com/paper/2201.05624/) (2022)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [A review on outlier/anomaly detection in time series data](https://mlsanity.com/paper/2002.04236/) (2020)
- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model](https://mlsanity.com/paper/1511.01644/) (2015)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspective](https://mlsanity.com/paper/1811.03402/) (2018)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Prediction](https://mlsanity.com/paper/2104.06344/) (2021)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Outcome-Oriented Predictive Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/1707.06766/) (2017)
- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning](https://mlsanity.com/paper/2212.03954/) (2022)
- [Constructing Narrative Event Evolutionary Graph for Script Event Prediction](https://mlsanity.com/paper/1805.05081/) (2018)
- [A systematic literature review on state-of-the-art deep learning methods for process prediction](https://mlsanity.com/paper/2101.09320/) (2021)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)
- [Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare](https://mlsanity.com/paper/2105.07542/) (2021)
- [Spatio-Temporal Data Mining: A Survey of Problems and Methods](https://mlsanity.com/paper/1711.04710/) (2017)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Uncertainty on Asynchronous Time Event Prediction](https://mlsanity.com/paper/1911.05503/) (2019)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Analogous Process Structure Induction for Sub-event Sequence Prediction](https://mlsanity.com/paper/2010.08525/) (2020)
- [Clustering-Based Predictive Process Monitoring](https://mlsanity.com/paper/1506.01428/) (2015)
- [Time and Activity Sequence Prediction of Business Process Instances](https://mlsanity.com/paper/1602.07566/) (2016)

## Built on by (most cited first, 2 of 2)

- [ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model](https://mlsanity.com/paper/2408.07840/) (2024)
- [Timing Matters: Enhancing User Experience through Temporal Prediction in Smart Homes](https://mlsanity.com/paper/2411.18719/) (2024)

## Abstract (arXiv)

Event prediction is the ability of anticipating future events, i.e., future real-world occurrences, and aims to support the user in deciding on actions that change future events towards a desired state. An event prediction method learns the relation between features of past events and future events. It is applied to newly observed events to predict corresponding future events that are evaluated with respect to the user's desired future state. If the predicted future events do not comply with this state, actions are taken towards achieving desirable future states. Evidently, event prediction is valuable in many application domains such as business and natural disasters. The diversity of application domains results in a diverse range of methods that are scattered across various research areas which, in turn, use different terminology for event prediction methods. Consequently, sharing methods and knowledge for developing future event prediction methods is restricted. To facilitate knowledge sharing on account of a comprehensive integration and assessment of event prediction methods, we take a systems perspective to integrate event prediction methods into a single system, elicit requirements, and assess existing work with respect to the requirements. Based on the assessment, we identify open challenges and discuss future research directions.