Existing forecasting methods often force a trade-off: either train a highly specialized model for each site (which is costly and doesn't scale) or adapt a large, general-purpose model (which can be ...

Abstract: Existing time-series forecasting methods often struggle to adapt to dynamic scenarios and lack flexibility in prediction. They typically require retraining the model when the prediction ...

Abstract: Time series forecasting plays a vital role in various fields such as energy forecasting and transportation planning. Although Transformer-based models have made remarkable progress in time ...

Annual inflation slowed in January, falling more than economists expected, helped by declining prices for gasoline and used vehicles. Cooler price increases overall are positive news for the economy, ...

(Editor's note: Kyle Busch earned pole position at the 2026 Daytona 500, while Chase Briscoe secured the other front-row spot at qualifying Feb. 11.) Welcome to Speedweek! That's the official name for ...

All models share a common interface exposed by model_factory.py for switching architectures by changing a CLI flag.

South Korean researchers have developed a guided-learning framework that accurately predicts PV power without requiring irradiance sensors during operation, using routine meteorological data instead.

ROCK SPRINGS — The 2026 Budget Session arrives at a pivotal moment for Wyoming. With a newly empowered Wyoming Freedom Caucus holding a House majority, local delegates are navigating deep divides over ...