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

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

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 ...

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

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 ...

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 ...

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.

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 ...

Recent advances in forecasting demand within emergency departments (EDs) have been bolstered by the integration of machine learning and time series analytical techniques. The objective of these ...

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 ...