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 ...

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

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

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 ...

The observational track of Typhoon "Danas" (solid line) along with forecasted paths (dashed lines) depicted on the FY-4B satellite visible light imagery at 08:00 BST on July 6, 2025. The dashed lines ...

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 ...

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 ...

Machine learning is reshaping the way portfolios are built, monitored, and adjusted. Investors are no longer limited to ...

While demand planning accuracy currently hovers around 60%, DLA officials aim to push that baseline figure to 85% with the help of AI and ML tools. Improved forecasting will ensure the services have ...