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

Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance. Machine learning models, by contrast, offer substantial flexibility ...

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.

This study was led by Professor Qi Zhong and Professor Xiuping Yao from the China Meteorological Administration Training Center, and Assistant Engineer Zhicha Zhang from the Zhejiang Meteorological ...

Machine learning models are taking over in the field of weather forecasting, from a quick “how long will this rain last” to a 10-day outlook, all the way out to century-level predictions. The ...

A new research paper shows the approach performs significantly better than the random-walk forecasting method.

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 ...

One of the unwritten axioms of data scientists specializing in machine learning methodologies is that they all try their hand at predicting the stock market. Some of the best attempts have turned a ...