Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

AI matches daily forecasts like never before, but when it comes to heat, cold, and wind records, HRES physics rules.

The landscape of demand forecasting, data science and machine learning is rapidly evolving, as companies seek innovative approaches to handle the intricate intersection between technology and consumer ...

Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. It's easy to forget that beneath the surface of every smart ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

MIAMI — The Atlantic hurricane season, which draws to an official close on Sunday, fulfilled forecasts it would be an active year. There were 13 named storms and three Category 5 hurricanes. But, for ...

We develop methodology to bridge scenario analysis and risk forecasting, leveraging their respective strengths in policy settings. The methodology, rooted in Bayesian analysis, addresses the ...

Legacy load forecasting models are struggling with ever-more-common, unpredictable events; power-hungry AI offers a solution.

Supply chain forecasting is becoming an increasingly critical component of operational success. Accurate forecasting enables companies to optimize inventory levels, reduce waste, enhance customer ...

My print column examines the proliferation of statistical forecasting models for the Academy Awards, which will be handed out on Sunday night. After quantitative predictions did well in forecasting ...