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

Abstract

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.

References in corpus (12)

• Time Series Forecasting With Deep Learning: A Survey
• Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods
• A Survey on the Explainability of Supervised Machine Learning
• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
• Deep Learning for Time Series Forecasting: Tutorial and Literature Survey
• A systematic literature review on state-of-the-art deep learning methods for process prediction
• Process Outcome Prediction: CNN vs. LSTM (with Attention)
• Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring
• How do I update my model? On the resilience of Predictive Process Monitoring models to change
• Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions
• Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
• Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring