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

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Business Process Management (BPM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12168))

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Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However, due to insufficient training data or sub-optimal network configuration and architecture, these approaches do not generalize well the problem at hand. This paper proposes a novel adversarial training framework to address this shortcoming, based on an adaptation of Generative Adversarial Networks (GANs) to the realm of sequential temporal data. The training works by putting one neural network against the other in a two-player game (hence the “adversarial” nature) which leads to predictions that are indistinguishable from the ground truth. We formally show that the worst-case accuracy of the proposed approach is at least equal to the accuracy achieved in non-adversarial settings. From the experimental evaluation it emerges that the approach 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. Moreover, the approach is more robust, as its accuracy is not affected by fluctuations over the case length.

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We thank Manuel Camargo and Vincenzo Pasquadibisceglie for providing access and instructions to use their tools. This research is partly funded by the Australian Research Council (DP180102839).

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Correspondence to Farbod Taymouri .

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Taymouri, F., Rosa, M.L., Erfani, S., Bozorgi, Z.D., Verenich, I. (2020). Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham.

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  • Print ISBN: 978-3-030-58665-2

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