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New research paper explores trade-offs in event sequence forecasting

A new research paper explores event sequence forecasting, a problem relevant to fields like finance and healthcare. The study evaluates forecasting methods based on both prediction accuracy and distributional realism, introducing a benchmark with metrics, baselines, and real-world datasets. Findings indicate a trade-off between accuracy and realism, but the paper also presents Wasserstein BaryBooster, an algorithm that converts realism from a diffusion model into improved prediction accuracy. AI

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research paper explores trade-offs in event sequence forecasting

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12 / 100
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The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Dmitry Osin, Egor Surkov, Petr Mokrov, Igor Udovichenko, Ivan Karpukhin, Evgeny Burnaev, Egor Shvetsov, Alexander Korotin ·

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

    arXiv:2610.12586v1 Announce Type: new Abstract: 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 fie…