Event forecasting stops ranking on accuracy alone: aggregating a diffusion model's realism beats dedicated predictors on TMS
Event sequence forecasting has long been ranked by prediction accuracy alone, even though generative models can produce many possible futures. A benchmark for heterogeneous event sequences such as card transactions measures accuracy and distributional realism together and finds no method leads on both: diffusion models lead on realism, dedicated predictors on accuracy. A post-hoc aggregation called Wasserstein BaryBooster collapses a diffusion model's samples into one trajectory, reaching the highest TMS on three datasets while keeping non-trivial cDFS.