Predicting Process Behaviour using Deep Learning

Abstract

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

34 pages, 10 figures

References in corpus (4)

• Deep Learning in Neural Networks: An Overview
• WaveNet: A Generative Model for Raw Audio
• Recurrent Neural Network Regularization
• Understanding Neural Networks Through Deep Visualization

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