The study evaluates how forecast lead time influences the effectiveness of outage predictions for crew deployment using AI weather models GraphCast and FourCastNet. It finds that the ability to capture outage burden decreases with longer lead times, with a notable drop between three and five days. Early deployment with all requested crews outperforms late deployment with fewer crews, and the study also examines the impact of antecedent land surface predictors and calibration of prediction intervals for tropical cyclone events.
By Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohamad Alipour
The paper introduces a data‑onboarding pipeline that detects and repairs defects in building meter data before training machine‑learning demand‑forecasting models. Controlled experiments on hourly electricity data from twelve U.S. buildings show that the pipeline restores forecast accuracy to clean‑data levels, even when defects affect up to 1.6% of training rows, and works across multiple models and horizons. The study also documents a first‑version over‑cleaning issue, its cause, and a per‑building calibration that corrects it, with all results reproducible from publicly pinned inputs.
By Yixuan Liang
arXiv:2610.31959v1 Announce Type: cross
Abstract: Short-term building load forecasting remains challenging because load patterns can change rapidly with weather and operating conditions. Data-driven...
By Shiqiao Zhou, Huanbo Lyu, Holger Sch\"oner, Zipeng Wu, Jessica Deuschel, Freya Wentzel, IAG Wilson, Shuo Wang
The paper proposes a shift from predictive modeling to descriptive provider behavior profiles for fraud, waste, and abuse (FWA) review. By decomposing billed revenue into provider scale and procedure composition, the authors construct lineage‑aware profiles that capture scale history, code lineage, and clinical family shares. In a large Medicare audit, these simple, interpretable descriptions outperform complex forecasts and improve recall for high‑cost rare events, while an optional semantic factorization adds context without inferring intent.
By Yubin Park, Evan Brociner
The paper introduces a lightweight pre‑encoder gate for Transformer‑based time‑series forecasters, which assigns sigmoid scores to covariate representations before they enter the encoder. The gate is evaluated as a plug‑in for models such as TimeXer, iTransformer, and PatchTST on datasets including ETTm1, ETTm2, Traffic, Energy, and ILI, showing competitive performance and the ability to regulate covariate admission via a usage penalty. Experiments also explore gate placement, initialization, and feature importance using VIF‑informed permutation diagnostics.
By Hongkai Zhuang, Tao Huang, Chen Hou
arXiv:2508. 04780v2 Announce Type: replace-cross Abstract: The increasing frequency of extreme weather events, such as hurricanes, highlights the urgent need for efficient and equitable power system restoration.
By Lin Jiang, Dahai Yu, Rongchao Xu, Tian Tang, Guang Wang
This study evaluates large language models (LLMs) for predicting weather‑related forced outage risk in a distribution grid using a zero‑shot approach without labeled training data. The task is framed as binary severity classification over 3h, 6h, and 12h horizons, leveraging six years of outage records and high‑resolution weather data from central Texas. Four zero‑shot LLMs are compared to two supervised classifiers under two input settings—current weather observations and forecast data—showing that supervised models lead on macro‑F1 and precision, while newer LLMs achieve competitive scores and offer complementary strengths in reasoning and geographic scalability.
By Christos Petridis, Zoran Obradovic, Mladen Kezunovic
The paper presents a system that combines coded police crash records with officer narratives to estimate uncoded crash factors in Texas from 2017‑2025. It uses an in‑context tabular foundation model (Kumo Tabular) to process coded data, a calibrated System One model (Jev) to read sampled narratives, and human judgments to recalibrate probabilities, producing validated population estimates for factors such as hydroplaning, medical episodes, fatigue, animals, and phone use. The approach demonstrates that narrative analysis uncovers more injury crashes than coded fields and that a single additional round of human review can significantly tighten estimate precision.
"whyItMatters":"The system provides safety offices with more accurate counts, a discordance map, a validated re‑read list, and a reading budget, enabling better resource allocation for road‑safety interventions."
By Amir Rafe, Subasish Das
arXiv:2608.24665v1 Announce Type: new
Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whe...
By Yamil Essus, Ranga Raju Vatsavai, Benjamin Rachunok
arXiv:2608. 02911v1 Announce Type: new Abstract: Revenue forecasts guide acquisition budgets, demand planning, and customer-based valuations, yet an aggregate forecast does not show whether change reflects acquisition, repeat purchasing, spending per order, or offsetting movements.
By Kyeongbin Kim, Daniel McCarthy, Dokyun Lee
The paper introduces paired, mechanism‑controlled stress tests that decompose changes in expected squared error for time‑series forecasting into environmental risk and forecast‑oracle distance. Using an origin‑conditioned predictive oracle, the authors validate three end‑to‑end controls and apply the benchmark to 24 forecasters, revealing that many models exhibit higher realized MSE yet lower oracle distance under frequent switching, and that environmental risk dominates in most scenarios. The study also demonstrates that visually compelling discovery profiles often fail to replicate on independent data‑generating process realizations, underscoring the importance of component‑wise diagnosis and held‑out stability audits.
By Xu Lin (Tsinghua University, Beijing, China), Runheng Zuo (Tsinghua University, Beijing, China), Shengxuan Xu (Tsinghua University, Beijing, China), Qitai Tan (Tsinghua University, Beijing, China), Hongyu Lin (Tsinghua University, Beijing, China), Xiao-Ping Zhang (Tsinghua University, Beijing, China)
arXiv:2511. 16839v4 Announce Type: replace-cross Abstract: Purpose: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinely collected electronic health records (EHRs) remains challenging.
By Falk Dippel, Yinan Yu, Annika Rosengren, Martin Lindgren, Christina E. Lundberg, Erik Aerts, Martin Adiels, Helen Sj\"oland