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statistical modeling

topic2 events
papersTODAY 04:00 UTC

Survey Maps Probabilistic Forecasting Methods for Time Series and Spatiotemporal Data

A new arXiv survey examines how probabilistic forecasting methods have developed across time series and spatiotemporal research. The authors argue the field has grown fragmented, with statistical modeling, machine learning, and deep generative approaches advancing largely in parallel. The paper aims to organize this landscape and connect the differing methodological traditions used for forecasting under uncertainty.

research/time-series/probabilistic-forecasting/survey
papersSEP 10 04:00 UTC

Paper shows fixed-rollout pass@k evaluations identify only limited information

A new paper examines the common practice of extrapolating pass@k benchmark results to attempt counts larger than the number of samples actually collected per problem. Under a pooled conditional-Binomial model, the authors show that success counts from fixed-size rollouts determine only a finite number of distribution moments. The result implies such evaluations cannot fully characterize model performance well beyond the sampled regime.

evaluation/benchmarks/statistics/llm-evaluation