In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...

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 ...

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.

On Tuesday, the peer-reviewed journal Science published a study that shows how an AI meteorology model from Google DeepMind called GraphCast has significantly outperformed conventional weather ...

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 ...

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...

Put down the pen and paper and shelve the spreadsheets. Artificial intelligence (AI) and advanced machine learning are the next-generation tools for demand forecasting in distribution. That was the ...

Artificial intelligence-driven algorithms can be used to better forecast models for natural disasters, saving lives and protecting property by rapidly analyzing massive data sets and identifying ...

Cyber threats are increasing in speed and complexity, driving the need for advanced detection techniques. Machine learning is ...