Predictive Process Monitoring
25 papers with code • 0 benchmarks • 1 datasets
A branch of predictive analysis that attempts to predict some future state of a business process.
Benchmarks
These leaderboards are used to track progress in Predictive Process Monitoring
Ultra-processed Food Dataset
Most implemented papers
Predictive Business Process Monitoring with LSTM Neural Networks
First, we show that LSTMs outperform existing techniques to predict the next event of a running case and its timestamp.
Alarm-Based Prescriptive Process Monitoring
Predictive process monitoring is concerned with the analysis of events produced during the execution of a process in order to predict the future state of ongoing cases thereof.
Outcome-Oriented Predictive Process Monitoring: Review and Benchmark
Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces.
Temporal Stability in Predictive Process Monitoring
irhete/stability-predictive-monitoring
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We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability.
Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring
Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances.
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction
farbodtaymouri/GanPredictiveMonitoring
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Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp.
Explainable Predictive Process Monitoring
PyRicky/LSTM_Generic_explainable
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Predictive Business Process Monitoring is becoming an essential aid for organizations, providing online operational support of their processes.
Evaluating Explainable Methods for Predictive Process Analytics: A Functionally-Grounded Approach
Current explainable machine learning methods, such as LIME and SHAP, can be used to interpret black box models.
Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes
hansweytjens/uncertainty-remaining_time
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This obliviousness of uncertainty is a major obstacle towards their adoption in practice.
Creating Unbiased Public Benchmark Datasets with Data Leakage Prevention for Predictive Process Monitoring
raseidi/cosmo
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Often the training and test sets are not completely separated, a data leakage problem particular to predictive process monitoring.
