The absence of reliable data on fundamental economic indicators (e.g. real GDP), combined with structural shifts in the economy, can severely constrain the ability to conduct accurate macroeconomic ...

Forbes contributors publish independent expert analyses and insights. Jesse Damiani covers AI, ClimateTech, and emerging media. Mar 03, 2025, 04:06pm EST Mar 04, 2025, 03:48pm EST Image of Hurricane ...

Tropical cyclones remain one of the most formidable natural hazards, intricately governed by a complex interplay of atmospheric and oceanic processes. Recent advancements have enhanced our ...

Forecasting inflation has become a major challenge for central banks since 2020, due to supply chain disruptions and economic uncertainty post-pandemic. Machine learning models can improve forecasting ...

Ph.D. student Phillip Si and Assistant Professor Peng Chen developed Latent-EnSF, a technique that improves how ML models assimilate data to make predictions.

Researchers in China have applied a machine learning technology based on temporal convolutional networks in PV power forecasting for the first time. The new model reportedly outperforms similar models ...

Picture a single forecasting mistake triggering a cascade of negative consequences, such as surplus inventory, strained supplier relationships and disappointed customers. In today's world, accurate ...

A range of national meteorological services across Europe and ECMWF have launched Anemoi, a framework for creating machine learning (ML) weather forecasting systems. Named after the Greek gods of the ...