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.

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 ...

Researchers have developed a feature selection-based solar irradiance forecasting method to improve the operation of stand-alone photovoltaic systems. The approach uses a bidirectional long short-term ...

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 ...

A University of Manchester researcher has developed a machine-learning method to spot sudden changes in fluid behavior, known as bifurcation points, before traditional simulations fail. The approach ...

AI-driven forecasting and optimization are transforming the automotive supply chain, improving demand accuracy, cutting inventory costs, and enhancing resilience. Studies highlight gains from ensemble ...

Discover what savvy investors evaluate before trusting a crypto price prediction, including methodology, data quality, ...