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Conference Papers Year : 2017

Time Series Petri Net Models


Operational support as an area of process mining aims to predict the performance of individual cases and the overall business process. Although seasonal effects, delays and performance trends are well-known to exist for business processes, there is up until now no prediction model available that explicitly captures seasonality. In this paper, we introduce time series Petri net models. These models integrate the control flow perspective of Petri nets with time series prediction. Our evaluation on the basis of our prototypical implementation demonstrates the merits of this model in terms of better accuracy in the presence of time series effects.
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Dates and versions

hal-01651885 , version 1 (29-11-2017)





Andreas Solti, Laura Vana, Jan Mendling. Time Series Petri Net Models. 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA), Dec 2015, Vienna, Austria. pp.124-141, ⟨10.1007/978-3-319-53435-0_6⟩. ⟨hal-01651885⟩
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