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

As climate extremes intensify across Africa, the need for accurate and timely weather prediction has become increasingly ...

Buildings produce a large share of New York's greenhouse gas emissions, but predicting future energy demand—essential for ...

With legacy load forecasting models struggling with unpredictable events that are becoming ever more common, power-hungry AI ...

Meteorologists and other environmental scientists rely on numerical forecast models to aid in developing a weather outlook. These models, such as the American GFS model and European ECMWF model, use ...

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 ...

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 ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...