Process Model Forecasting Using Time Series Analysis of Event Sequence Data
This work fills a notable void in process analytics by enabling forecasts at the model level, which is incremental as it extends existing instance-level prediction techniques to a broader scope.
The paper addresses the lack of process model-level forecasting by developing a technique to predict entire process models from historical event data, representing probable future states to analyze drift and bottlenecks, with demonstrated accuracy on real-world data.
Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models. At the instance level, various novel techniques have been recently devised, tackling next activity, remaining time, and outcome prediction. At the model level, there is a notable void. It is the ambition of this paper to fill this gap. To this end, we develop a technique to forecast the entire process model from historical event data. A forecasted model is a will-be process model representing a probable future state of the overall process. Such a forecast helps to investigate the consequences of drift and emerging bottlenecks. Our technique builds on a representation of event data as multiple time series, each capturing the evolution of a behavioural aspect of the process model, such that corresponding forecasting techniques can be applied. Our implementation demonstrates the accuracy of our technique on real-world event log data.