Temporal Stability in Predictive Process Monitoring

Noname manuscript No. (will be inserted by the editor) Abstract Predictive business process monitoring is concerned with the analysis of events produced during the execution of a business process in order to predict as early as possible the final outcome of an ongoing case. Traditionally, predictive process monitoring methods are optimized with respect to accuracy. However, in environments where users make decisions and take actions in response to the predictions they receive, it is equally important to optimize the stability of the successive predictions made for each case. To this end, this paper defines a notion of temporal stability for predictive process monitoring and evaluates existing methods with respect to both temporal stability and accuracy. We find that methods based on XGBoost and LSTM neural networks exhibit the highest temporal stability. We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability. Finally, we show that time series smoothing techniques can further enhance temporal stability at the expense of slightly lower accuracy. Keywords Predictive Monitoring · Early Sequence Classification · Stability 1 Introduction Modern organizations generally execute their business processes on top of processaware information systems, such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and Business Process Management Systems (BPMS), among others [8]. These systems record a range of events that occur during the execution of the processes they support, including events signaling the creation and completion of business process instances (herein called cases) and the start and completion of activities within each case. Event records produced by process-aware information systems can be extracted and pre-processed to produce business process event logs [1]. A business process event log consists of a set of traces, each trace consisting of the sequence of event records produced by one case. Each event record has a number of attributes. Three of these attributes are present in every event record, namely the event class (a.k.a. In other words, every event represents the occurrence of an activity at a particular point in time and in the context of a given case.