Actor-Enriched Time Series Forecasting of Process Performance
This work addresses the need for more accurate performance predictions in process mining to support proactive decision-making, representing an incremental improvement by extending existing methods with actor-centric features.
The study tackled the problem of improving throughput time forecasting in predictive process monitoring by incorporating actor behavior as time-varying signals, and found that actor-enriched models consistently outperformed baseline models in terms of RMSE, MAE, and R2 metrics.
Predictive Process Monitoring (PPM) is a key task in Process Mining that aims to predict future behavior, outcomes, or performance indicators. Accurate prediction of the latter is critical for proactive decision-making. Given that processes are often resource-driven, understanding and incorporating actor behavior in forecasting is crucial. Although existing research has incorporated aspects of actor behavior, its role as a time-varying signal in PPM remains limited. This study investigates whether incorporating actor behavior information, modeled as time series, can improve the predictive performance of throughput time (TT) forecasting models. Using real-life event logs, we construct multivariate time series that include TT alongside actor-centric features, i.e., actor involvement, the frequency of continuation, interruption, and handover behaviors, and the duration of these behaviors. We train and compare several models to study the benefits of adding actor behavior. The results show that actor-enriched models consistently outperform baseline models, which only include TT features, in terms of RMSE, MAE, and R2. These findings demonstrate that modeling actor behavior over time and incorporating this information into forecasting models enhances performance indicator predictions.