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

• URL: http://arxiv.org/abs/2302.04018v1
• Date: Wed, 8 Feb 2023 12:21:02 GMT
• Title: A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas
• Authors: Janik-Vasily Benzin, Stefanie Rinderle-Ma
• Abstract summary: Event prediction aims to support the user in deciding on actions that change future events towards a desired state. The diversity of application domains results in a diverse range of methods that are scattered across various research areas. To facilitate knowledge sharing on account of a comprehensive classification, integration, and assessment of event prediction methods, we combine and take a systems perspective.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 classification, integration, and assessment of event prediction methods, we combine taxonomies and 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.

Related papers

• Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
  Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types. We aim to improve zero-shot event detection by training models to better follow event definitions.
  arXiv  Detail & Related papers  (2024-03-05T01:46:50Z)
• Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media [29.22073623882667]
  We introduce Future ADaptive Event-based Fake news Detection (FADE) framework. We train a target predictor through an adaptive augmentation strategy and graph contrastive learning to make more robust overall predictions. We further mitigate event bias by subtracting the output of the event-only predictor from the output of the target predictor.
  arXiv  Detail & Related papers  (2024-02-29T06:40:53Z)
• A Discussion on Generalization in Next-Activity Prediction [1.2289361708127877]
  We show that there is an enormous amount of example leakage in all of the commonly used event logs. We argue that designing robust evaluations requires a more profound conceptual engagement with the topic of next-activity prediction.
  arXiv  Detail & Related papers  (2023-09-18T09:42:36Z)
• Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment [72.50906475214457]
  The goal of sequential event prediction is to estimate the next event based on a sequence of historical events. In practice, the next-event prediction models are trained with sequential data collected at one time. We propose a framework with hierarchical branching structures for learning context-specific representations.
  arXiv  Detail & Related papers  (2022-10-24T07:54:13Z)
• A-ACT: Action Anticipation through Cycle Transformations [89.83027919085289]
  We take a step back to analyze how the human capability to anticipate the future can be transferred to machine learning algorithms. A recent study on human psychology explains that, in anticipating an occurrence, the human brain counts on both systems. In this work, we study the impact of each system for the task of action anticipation and introduce a paradigm to integrate them in a learning framework.
  arXiv  Detail & Related papers  (2022-04-02T21:50:45Z)
• Instance-Aware Predictive Navigation in Multi-Agent Environments [93.15055834395304]
  We propose an Instance-Aware Predictive Control (IPC) approach, which forecasts interactions between agents as well as future scene structures. We adopt a novel multi-instance event prediction module to estimate the possible interaction among agents in the ego-centric view. We design a sequential action sampling strategy to better leverage predicted states on both scene-level and instance-level.
  arXiv  Detail & Related papers  (2021-01-14T22:21:25Z)
• Adversarial Generative Grammars for Human Activity Prediction [141.43526239537502]
  We propose an adversarial generative grammar model for future prediction. Our grammar is designed so that it can learn production rules from the data distribution. Being able to select multiple production rules during inference leads to different predicted outcomes.
  arXiv  Detail & Related papers  (2020-08-11T17:47:53Z)
• Event Prediction in the Big Data Era: A Systematic Survey [7.3810864598379755]
  Event prediction is becoming a viable option in the big data era. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction.
  arXiv  Detail & Related papers  (2020-07-19T23:24:52Z)
• Cause vs. Effect in Context-Sensitive Prediction of Business Process Instances [0.440401067183266]
  This paper addresses the issue of context being cause or effect of the next event and its impact on next event prediction. We leverage previous work on probabilistic models to develop a Dynamic Bayesian Network technique. We evaluate our technique with two real-life data sets and benchmark it with other techniques from the field of predictive process monitoring.
  arXiv  Detail & Related papers  (2020-07-15T08:58:15Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.