Process analytics is the field focusing on 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.

中文翻译：

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使用事件序列数据的时间序列分析进行过程模型预测

流程分析是一个专注于单个流程实例或整体流程模型的预测的领域。在实例级别，最近已经设计出各种新颖的技术来应对下一个活动，剩余时间和结果预测。在模型级别，存在明显的空白。填补这一空白是本文的雄心。为此，我们开发了一种从历史事件数据预测整个过程模型的技术。预测模型是一个将要表示的过程模型，代表整个过程的未来可能状态。这样的预测有助于调查漂移和新出现的瓶颈的后果。我们的技术以事件数据作为多个时间序列的表示为基础，每个时间序列都捕获了流程模型的行为方面的演变，这样就可以应用相应的预测技术。我们的实现演示了我们的技术在真实事件日志数据上的准确性。