Artificial intelligence hurricane forecast models learn as they go, sharpened 2025 predictions and helped people act sooner ...

This repository contains the official source code and data for the research paper: "Enhancing stock price forecasting with a modular deep learning framework incorporating plug-and-play Transformer ...

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This Python package implements algorithms for online learning under delay using optimistic hints. More details on the algorithms and their regret properties can be found in the manuscript Online ...

Google DeepMind and Google Research today announced WeatherNext 2 as its “most advanced and efficient forecasting model.” Notably, it’s helping power forecasts in Google’s consumer apps, including ...

The increasing demand for renewable energy sources has positioned solar energy as a pivotal component in the global transition towards sustainable power generation. As the demand increases for solar ...

Accurate demand forecasting is essential for informed decision-making in today’s dynamic business environment, where product demand often follows diverse and shifting patterns throughout increasingly ...

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In an era where data drives decisions, the ability to forecast the future isn’t just a nice-to-have—it’s essential. Time series forecasting, once limited to specialized statisticians and analysts, is ...

Abstract: Accurate photovoltaic (PV) power forecasting remains critical for grid stability but is challenged by weather uncertainties and the inability of traditional methods to effectively integrate ...

Weather forecasting has gone through incremental but tremendous progress in past decades. By one metric, today's five-day forecast is now as accurate as a three-day forecast was in 2000. Entire ...

Destructive hurricanes. Huge ocean waves. Sandstorms and severe smog. Extreme weather events are becoming increasingly common, piling pressure on communities to prepare for a range of disasters with ...