Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective

Event sequences are chains of random events, each described by heterogeneous (numerical and categorical) features. The problem of event sequence forecasting occurs in finance, healthcare, business process intelligence, and other fields. In this work, we study long-horizon forecasting of event sequences as a conditional generative modeling problem. We evaluate forecast quality from two perspectives: prediction accuracy and distributional realism. To support this, we present a benchmark that contributes metrics for both aspects, strong statistical baselines, recent generative models adapted from adjacent fields, and curated real-world datasets. Careful evaluation reveals that no method excels at both accuracy and realism, highlighting a fundamental trade-off between the two. Finally, we introduce a procedure that aggregates samples from a realism-strong generative model into a state-of-the-art predictor, demonstrating that distributional realism can be converted into prediction accuracy.