Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors

• URL: http://arxiv.org/abs/2206.13109v1
• Date: Mon, 27 Jun 2022 08:27:35 GMT
• Title: Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors
• Authors: Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt
• Abstract summary: We extend remaining time prediction based on Petri nets with generally distributed transitions with k-nearest neighbors. The k-nearest neighbors algorithm is performed on simple storing the time passed to complete previous activities. We discuss the technique and its basic implementation in Python and use different real world data sets to evaluate the predictive power of our extension.
• Score: 0.5287304201523223
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable remaining time prediction of ongoing business processes is a highly relevant topic. One example is order delivery, a key competitive factor in e.g. retailing as it is a main driver of customer satisfaction. For realising timely delivery, an accurate prediction of the remaining time of the delivery process is crucial. Within the field of process mining, a wide variety of remaining time prediction techniques have already been proposed. In this work, we extend remaining time prediction based on stochastic Petri nets with generally distributed transitions with k-nearest neighbors. The k-nearest neighbors algorithm is performed on simple vectors storing the time passed to complete previous activities. By only taking a subset of instances, a more representative and stable stochastic Petri Net is obtained, leading to more accurate time predictions. We discuss the technique and its basic implementation in Python and use different real world data sets to evaluate the predictive power of our extension. These experiments show clear advantages in combining both techniques with regard to predictive power.

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