The paper introduces Empirical Research Assistance (ERA), an autonomous system that employs Large Language Model‑guided tree search to create, evaluate, and refine executable forecasting software for infectious diseases. In a real‑time evaluation during the 2025‑26 U.S. respiratory season, ERA produced diverse models for influenza, COVID‑19, and RSV, and an ensemble of these models outperformed the CDC’s gold‑standard forecast ensembles across all three pathogens. ERA also demonstrated robustness in data‑scarce RSV scenarios and showed that higher‑tier language models and specific optimization metrics can improve forecast accuracy.
By Sarah Martinson, Michael P. Brenner, Martyna Plomecka, Brian P. Williams, Nicholas G. Reich, Zahra Shamsi
arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair
The study benchmarks various forecasting models—including statistical, machine‑learning, recurrent neural networks, and pretrained TimesFM—for day‑ahead electricity demand in a university building. TimesFM 3.0 with weather covariates achieves the lowest overall mean absolute error, outperforming a weekly seasonal‑naive baseline by about 43%, while XGBoost remains competitive and requires only local fitting. The LSTM model takes longer to train without improving accuracy, and TimesFM’s performance is robust even without covariates, though covariates slightly improve holiday forecasts.
By Amey Vasulkar, Samuel Kernan Freire, Arie-Willem de Leeuw, Tadeo-Baldiri Salcedo-Rahola
arXiv:2603.25495v2 Announce Type: replace-cross
Abstract: Accurate short-term air-quality forecasting is essential for public health protection and urban management, yet many recent forecasting frame...
By Moazzam Umer Gondal, Hamad ul Qudous, Asma Ahmad Farhan, Sultan Alamri
arXiv:2606. 19560v1 Announce Type: new Abstract: Seasonal influenza infects millions of people and causes substantial morbidity and mortality in the United States each year, making accurate short-term forecasting a core public-health need.
By Alireza Jafari, Judy Fox, Geoffrey C. Fox, Madhav Marathe, Aniruddha Adiga
arXiv:2606. 15058v1 Announce Type: new Abstract: This study examines whether machine learning (ML) models can outperform the naive random walk benchmark in forecasting the monthly USD/CAD exchange rate.
By Louis Agyekum, Edmund Fosu Agyemang, Obu-Amoah Ampomah, Kofi Acheampong, Emmanuel Boadi, Priscilla Yaa Amakye, Fafa Shalom Tchorly, Enock Adu Bonsu, Eric Nyarko
arXiv:2606. 20918v2 Announce Type: replace-cross Abstract: Accurate short-term electricity demand forecasting is critical for reliable power system operation, energy market planning, and infrastructure optimization.
By Reza Ghanavati, Behrooz Mosallaei
arXiv:2602. 02288v3 Announce Type: replace Abstract: Current time-series forecasting models are primarily based on transformer-style neural networks.
By Zheng Li, Jerry Cheng, Huanying Gu
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 introduces Generalized Gibbs Ensemble Weighting (GGEW), a probabilistic framework that assigns weights to forecasting models using a Gibbs-style exponential transformation of normalized predictive loss. GGEW extends basic weighting through numerical stabilization, diversity-aware score corrections, and online hyperparameter adaptation, yielding variants such as Stable Gibbs weighting, Directional Gibbs-NCL, and Symmetric Gibbs-NCL. The authors evaluate GGEW on M4 competition submissions and real-world datasets (Monash Traffic, Electricity, Solar), finding that Gibbs-style adaptive weighting is competitive across various settings, though performance varies by dataset, horizon, and deployment protocol.
By Prasen R. Nuthanakaluva, Nava K. Gaddam
arXiv:2608. 20024v1 Announce Type: new Abstract: District heating energy hubs require reliable heat load forecasts for efficient operational scheduling.
By Ben Spoek, Karim K. Ben Hicham, Kai Derzsi, Philipp Althaus, Alexander Mitsos, Dirk M\"uller
arXiv:2509. 26468v3 Announce Type: replace Abstract: Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly with the rise of pretrained models.
By Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen, Lorenzo Stella, Nick Erickson, Pablo Guerron, Michael Bohlke-Schneider, Yuyang Wang