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.
This research explores the trade-off between prediction accuracy and distributional realism in event sequence forecasting, proposing a benchmark and a novel algorithm to address this challenge.
What happened
The study evaluates multi-horizon event sequence forecasting methods, demonstrating that accuracy and distributional realism are conflicting objectives. It introduces a benchmark with statistical baselines, real-world datasets, and a novel algorithm (Wasserstein BaryBooster) to improve accuracy through realism.
Why it matters
Balancing accuracy and realism is critical for applications in finance, healthcare, and business processes where both precise predictions and statistically valid distributions are required.
- Accuracy and realism in forecasting are inherently conflicting objectives
- Wasserstein BaryBooster demonstrates how realism can enhance accuracy
- The introduced benchmark provides a standardized evaluation framework
- Event sequence forecasting requires careful consideration of distributional properties
The bigger picture
The findings highlight the need for methodological trade-offs in forecasting systems, while the proposed algorithm offers a practical approach to improve predictive performance through realism-driven techniques.
This work advances understanding of event sequence forecasting by identifying key trade-offs and introducing a method to leverage distributional realism for improved predictive accuracy.
Technical details
- Conditional generative modeling for event sequence forecasting
- Wasserstein BaryBooster: sample aggregation from diffusion models
- Heterogeneous feature handling (numerical and categorical data)
- Multi-horizon forecasting frameworks
FAQ
What is event sequence forecasting?
It involves predicting chains of events with heterogeneous features (numerical and categorical) in domains like finance and healthcare.
What is Wasserstein BaryBooster?
A novel algorithm that combines samples from a realism-focused diffusion model to achieve state-of-the-art prediction accuracy.
Why is there a trade-off between accuracy and realism?
The study shows that methods optimized for one objective (accuracy or realism) perform suboptimally on the other, requiring careful balancing.
This summary and analysis were generated with AI from the original article and may contain errors. Read the original for the full story.
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Originally published by arXiv cs.LG on Oct 12, 2026.
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