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

Key points

• The problem of event sequence forecasting occurs in finance, healthcare, business process intelligence, and other fields.1
• In this work, we study multi-horizon forecasting of event sequences as a conditional generative modeling problem.1
• We evaluate forecasts not only for prediction accuracy, but also for distributional realism.1
• Finally, we introduce Wasserstein BaryBooster, an algorithm that aggregates samples from a realism-strong diffusion model into a state-of-the-art predictor, demonstrating that distributional realism can be converted into prediction accuracy.1

Sources (1)

• [1]Realism VS Accuracy: Event Sequence Forecasting from a Generative Modeling Perspective
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 06:07 PM

  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.

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