Time series forecasting requires simplifying complex environments into quantifiable variables. These simplifications, while ...

According to IBM, attention is not all you need when forecasting certain outcomes with generative AI. You also need time. Earlier this year, IBM made its open-source TinyTimeMixer (TTM) model ...

Recent advances in AI, such as foundation models, make it possible for smaller companies to build custom models to make predictions, reduce uncertainty, and gain business advantage. Time series ...

In the 21st century, as global trade expands and cargo volumes surge, ports face mounting pressure to operate efficiently. A key challenge lies in accurately predicting vessel turnaround time ...

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 Atlantic hurricane season is drawing to a close, and with the tropics quieting down for a winter slumber, the focus of forecasters turns to evaluating what worked and what did not during the ...

Researchers in China conceived a new PV forecasting approach that integrates causal convolution, recurrent structures, attention mechanisms, and the Kolmogorov–Arnold Network (KAN). Experimental ...