Accurate forecasting of time series data is essential in many fields. However, real-world time series are often characterized by noise, non-stationarity and multiscale temporal dependencies, which ...

In the world around us, many things exist in the context of time: a bird's path through the sky is understood as different positions over a period of time, and conversations as a series of words ...

To enhance the dynamic perception and accuracy of tourism demand forecasting in smart tourism scenarios, this paper proposes a forecasting framework integrating a spatial econometric model and deep ...

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