mlsanityData to 7 Oct 2026

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

arXiv 2302.04018 ↗First posted 8 Feb 2023cs.AI cs.LGcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 65 of its 387 references that are in the map. First posted on arXiv 8 Feb 2023.

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65 of 387 references.

References · 65

  1. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  2. 2019Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methodscited by 903
  3. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  4. 2020Time Series Forecasting With Deep Learning: A Surveycited by 457
  5. 2020A Survey on the Explainability of Supervised Machine Learningcited by 225
  6. 2020A review on outlier/anomaly detection in time series datacited by 187
  7. 2019Deep Learning for Spatio-Temporal Data Mining: A Surveycited by 171
  8. 2015Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction modelcited by 140
  9. 2020Deep Learning for Time Series Forecasting: Tutorial and Literature Surveycited by 117
  10. 2018A Survey on Data Collection for Machine Learning: a Big Data -- AI Integration Perspectivecited by 111
  11. 2016Predictive Business Process Monitoring with LSTM Neural Networkscited by 103
  12. 2020Machine Learning Advances for Time Series Forecastingcited by 49
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  1. 2020Deep Learning for Predictive Business Process Monitoring: Review and Benchmarkcited by 45
  2. 2018Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoringcited by 43
  3. 2021The Future is not One-dimensional: Complex Event Schema Induction by Graph Modeling for Event Predictioncited by 39
  4. 2013Predictive Monitoring of Business Processescited by 37
  5. 2017Outcome-Oriented Predictive Process Monitoring: Review and Benchmarkcited by 37
  6. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  7. 2021ProcessTransformer: Predictive Business Process Monitoring with Transformer Networkcited by 34
  8. 2018Constructing Narrative Event Evolutionary Graph for Script Event Predictioncited by 33
  9. 2021A systematic literature review on state-of-the-art deep learning methods for process predictioncited by 32
  10. 2020Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Predictioncited by 31
  11. 2021Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcarecited by 30
  12. 2017Spatio-Temporal Data Mining: A Survey of Problems and Methodscited by 29
  13. 2018Predictive Process Monitoring Methods: Which One Suits Me Best?cited by 27
  14. 2019Uncertainty on Asynchronous Time Event Predictioncited by 27
  15. 2020Explainable Predictive Process Monitoringcited by 27
  16. 2020Analogous Process Structure Induction for Sub-event Sequence Predictioncited by 26
  17. 2015Clustering-Based Predictive Process Monitoringcited by 24
  18. 2016Time and Activity Sequence Prediction of Business Process Instancescited by 24
  19. 2020Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoringcited by 20
  20. 2021Automated Verification and Synthesis of Stochastic Hybrid Systems: A Surveycited by 20
  21. 2021Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphscited by 20
  22. 2017LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instancescited by 19
  23. 2019Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoringcited by 18
  24. 2021Prescriptive Process Monitoring for Cost-Aware Cycle Time Reductioncited by 17
  25. 2021Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Predictioncited by 15
  26. 2018An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Predictioncited by 14
  27. 2020Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoringcited by 14
  28. 2017Scalable Joint Models for Reliable Uncertainty-Aware Event Predictioncited by 13
  29. 2020PCNN: Deep Convolutional Networks for Short-term Traffic Congestion Predictioncited by 12
  30. 2020Event Prediction in the Big Data Era: A Systematic Surveycited by 12
  31. 2021Process Outcome Prediction: CNN vs. LSTM (with Attention)cited by 12
  32. 2021Integrating Deep Event-Level and Script-Level Information for Script Event Predictioncited by 11
  33. 2014'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicatorscited by 10
  34. 2021Text-Aware Predictive Monitoring of Business Processescited by 9
  35. 2020Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturingcited by 7
  36. 2021How do I update my model? On the resilience of Predictive Process Monitoring models to changecited by 7
  37. 2021Evaluating Predictive Business Process Monitoring Approaches on Small Event Logscited by 6
  38. 2020Cause vs. Effect in Context-Sensitive Prediction of Business Process Instancescited by 5

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