Visualizing Feature Importance of Time Series Data in Discrete-Event Simulations using Shapley Additive Explanations

As simulation applications become vital for understanding and predicting complex systems, analyzing data from repeated simulation runs is essential to gauge model uncertainty and identify optimal parameter settings. This paper presents a visualization tool for analyzing time series ensemble data generated by discrete-event simulations, focusing on feature importance within clustering results. The tool combines dimensionality reduction, clustering, and SHapley Additive exPlanations (SHAP) to highlight influential features and identify trends within clustered simulation data, advancing previous approaches focusing solely on visualization or clustering without analyzing specific feature contributions. By analyzing a manufacturing use case, we show how the visualization supports decision-makers by depicting the main features driving cluster formation and displaying time intervals critical to characterizing distinct system behaviors.