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time-series-11

Predictive Process Monitoring

3260 papers • 126 benchmarks • 313 datasets

A branch of predictive analysis that attempts to predict some future state of a business process.

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Datasets

Ultra-processed Food Dataset

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Most implemented papers

Predictive Business Process Monitoring with LSTM Neural Networks

Niek Tax, I. Verenich, M. Rosa, M. Dumas•Tue Dec 06 2016

This paper investigates Long Short-Term Memory neural networks as an approach to build consistently accurate models for a wide range of predictive process monitoring tasks and shows that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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Alarm-Based Prescriptive Process Monitoring

Niek Tax, M. Dumas, Irene Teinemaa, M. Leoni, F. Maggi•Thu Mar 22 2018

A framework for prescriptive process monitoring is proposed, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects and incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptives process monitoring in a given setting.

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HOEG: A New Approach for Object-Centric Predictive Process Monitoring

Tim K. Smit, H. Reijers, Xixi Lu•Sun Apr 07 2024

This work proposes the Heterogeneous Object Event Graph encoding (HOEG), which integrates events and objects into a graph structure with diverse node types, thus creating a more nuanced and informative representation.

5 0

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Temporal stability in predictive process monitoring

M. Dumas, Irene Teinemaa, F. Maggi, A. Leontjeva•Mon Dec 11 2017

This paper defines a notion of temporal stability for binary classification tasks in predictive process monitoring and evaluates existing methods with respect to both temporal stability and accuracy and finds that methods based on XGBoost and LSTM neural networks exhibit the highest temporal stability.

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Fire now, fire later: alarm-based systems for prescriptive process monitoring

Niek Tax, M. Dumas, Irene Teinemaa, M. Leoni, F. Maggi, Stephan A. Fahrenkrog-Petersen, M. Weidlich•Wed May 22 2019

A framework for prescriptive process monitoring is proposed, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect and incorporates a parameterized cost model to assess the cost–benefit trade-off of generating alarms.

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Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction

S. Erfani, I. Verenich, M. Rosa, Farbod Taymouri, Z. Bozorgi•Tue Mar 24 2020

A novel adversarial training framework based on an adaptation of Generative Adversarial Networks to the realm of sequential temporal data is proposed, which systematically outperforms all baselines both in terms of accuracy and earliness of the prediction, despite using a simple network architecture and a naive feature encoding.

101 0

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Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach

C. Ouyang, M. Velmurugan, Renuka Sindhgatta, C. Moreira•Mon Dec 07 2020

This paper draws on evaluation measures used in the field of explainable AI and proposes functionally-grounded evaluation metrics for assessing explainable methods in predictive process analytics and applies the proposed metrics to evaluate the performance of LIME and SHAP in interpreting process predictive models built on XGBoost.

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Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes

Jochen De Weerdt, Hans Weytjens•Tue May 11 2021

This paper is the first to apply Bayesian neural networks' uncertainty estimates themselves to predictive process monitoring and found that they contribute towards more accurate predictions and work quickly.

8 0

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Predictive Process Monitoring | State-of-the-Art