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 ...

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.

Weather forecasting is not easy. The truth is that predicting future weather conditions over broad, or even narrow, swaths of Earth's surface comes down to complex microphysical processes, and as ...

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 ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

Business planning has always involved some level of forecasting. Companies look at previous sales, market trends, customer behaviour, costs, and other business information to estimate what might ...

Preeclampsia with severe features remains a dangerous threat during pregnancy—especially in low-resource settings like ...

The landscape of demand forecasting, data science and machine learning is rapidly evolving, as companies seek innovative approaches to handle the intricate intersection between technology and consumer ...

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 ...