EventFlow applies flow matching to learn joint distributions over event times for temporal point processes, reporting 20-53% lower forecast error than autoregressive baselines on standard TPP benchmarks with fewer sampling calls.
On L ewis' simulation method for point processes
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Bayesian EVT with Hawkes-AR-Gumbel dependence estimates CVaR up to 99.995% on simulated operational risk data and outperforms independent and shared-factor baselines.
citing papers explorer
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EventFlow: Forecasting Temporal Point Processes with Flow Matching
EventFlow applies flow matching to learn joint distributions over event times for temporal point processes, reporting 20-53% lower forecast error than autoregressive baselines on standard TPP benchmarks with fewer sampling calls.
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Bayesian Extreme Value Theory with Hawkes-AR-Gumbel Dependence for Extreme CVaR Estimation in Operational Risk
Bayesian EVT with Hawkes-AR-Gumbel dependence estimates CVaR up to 99.995% on simulated operational risk data and outperforms independent and shared-factor baselines.