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 ...

Seasonal climate forecasting is important for societal welfare, as it supports decision-makers in taking proactive steps to mitigate risks from adverse climate conditions or to take advantage of ...

Successful test results of a new machine learning (ML) technique developed at Georgia Tech could help communities prepare for extreme weather and coastal flooding. The approach could also be applied ...

The COVID-19 pandemic emphasized the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy. Forecasting epidemic progression is a ...

Space weather forecasting remains a major challenge in heliophysics, as geomagnetic storms continue to pose significant risks to satellite operations, power ...

Artificial intelligence has become one of the most sought-after skills in the modern workforce. Organisations across industries are investing heavily in AI, machine learning, automation, and ...

FirstQFM, a pioneer in machine learning foundation models for quantum computing, announces a significant milestone in the commercial application of quantum computing today at the ISC High Performance ...

The 2025 hurricane season was a coming-of-age story for AI weather models, which have been around in some capacity since ...

A new system for forecasting weather and predicting future climate uses artificial intelligence (AI) to achieve results comparable with the best existing models while using much less computer power, ...

Integrated forecasting and supply chain planning platforms can close the gap between forecasting and execution by connecting ...

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.