The paper introduces WDANet, a frequency‑aware forecasting framework that uses stationary wavelet decomposition, FiLM, and a dual‑branch encoder‑decoder to separately model trend and fluctuation components in typhoon gust prediction. Applied to offshore Western Pacific wind data, WDANet outperforms ECMWF‑HRES for short lead times, achieving higher accuracy within the first 6 hours and better RMSE/MAE during extreme wind events. The study suggests WDANet could improve offshore wind power operations, disaster warnings, and risk mitigation.
By Xuefei Wang, Tingyi Liu, Heng Zhang, Shengjun Zhang
arXiv:2607. 27152v1 Announce Type: new Abstract: Accurate intraday forecasts of offshore wind are becoming increasingly important for power system operation and the integration of growing shares of offshore wind energy.
By Francesco Pinto, Luca Lanzilao, Paco Lopez Dekker, Angela Meyer
arXiv:2607. 10208v1 Announce Type: cross Abstract: Accurate meteorological forecasting is essential for agricultural planning, irrigation management, and environmental decision support.
By Piotr Sikora, Sotirios Kontogiannis
arXiv:2609.14733v1 Announce Type: new
Abstract: Hybrid Deep Learning for equity index forecasting is limited by three problems: propagation of OHLCV noise into derived technical indicators (TIs), cha...
By Aashish Bohra, Vivek Vijay
arXiv:2606. 19026v1 Announce Type: cross Abstract: Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced circulations, and other vertically structured atmospheric phenomena.
By David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia, Nick P. Bassill, Chris D. Thorncroft
arXiv:2606. 02849v1 Announce Type: new Abstract: Interval wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it accounts for the inherent uncertainty of wind resources.
By Vinicius Bortolini, Gilson Adamczuk Oliveira, Erick Oliveira Rodrigues, Matheus Henrique Dal Molin Ribeiro
arXiv:2506.12809v2 Announce Type: replace
Abstract: The long horizon forecasting (LHF) problem has come up in the time series literature for over the last 35 years or so. This review covers aspects o...
By Hans Krupakar, Kandappan V A
arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.
By Matteo Peduto, Qidong Yang, Jonathan Giezendanner, Devis Tuia, Sherrie Wang
arXiv:2606. 08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables.
By Dandan Chen, Yaqiang Wang
WaveletDiff is a diffusion-based framework that generates time series by training directly on wavelet coefficients, leveraging multi-resolution structure through level‑specific transformers and cross‑level attention with adaptive gating. The model incorporates Parseval‑theorem‑based energy constraints to preserve time‑frequency properties during diffusion. Experiments on six real‑world datasets from energy, finance, and neuroscience show that WaveletDiff outperforms several diffusion baselines and competes with the VAE/transformer‑based MSDformer, achieving lower discriminative and Context‑FID scores while using fewer parameters and less training time.
By Yu-Hsiang Wang, Olgica Milenkovic
arXiv:2607. 17095v1 Announce Type: new Abstract: Accurate short-term wind power forecasting is essential for grid stability and operational planning, yet remains challenging due to the complex interactions between atmospheric conditions and turbine dynamics.
By Shiyuan Piao, Fan Zehui, Yang Liu, Hong Cheng, Juepeng Zheng, Jie Zhou, Fugee Tsung
arXiv:2606. 17659v1 Announce Type: new Abstract: This study introduces enhancements to physics-constrained neural networks (PCNNs) that improve the accuracy and stability of hybrid short-term weather forecasting models.
By Egor Bugaev, Fedor Buzaev, Dmitry Efremenko, Denis Derkach, Fedor Ratnikov