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.

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 ...

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 ...

As the world grapples with the energy crisis and environmental concerns, the focus on renewable energy sources has intensified. Lithium-ion batteries, with their high energy density and low pollution, ...

The ITU Journal on Future and Evolving Technologies continues its in-depth coverage of machine learning for 5G and future networks.

IMF researchers show that satellite data, especially nighttime lights combined with machine learning can reliably estimate ...

Blue Water Intelligence (BWI) has launched machine learning-enabled river flow forecasting and early warning services in ...

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 ...

Anar Mammadov is the CEO of Senpex Technology and a software development professional with over 18 years experience in enterprise solutions. Managing the modern supply chain requires coping with ...