The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

Floods are a devastating natural calamity that may seriously harm both infrastructure and
people. Accurate flood forecasts and control are essential to lessen these effects and …

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Traffic forecasting is important for the success of intelligent transportation systems. Deep
learning models, including convolution neural networks and recurrent neural networks, have …

Many representative graph neural networks, $ eg $, GPR-GNN and ChebNet, approximate
graph convolutions with graph spectral filters. However, existing work either applies …

Designing spectral convolutional networks is a challenging problem in graph learning.
ChebNet, one of the early attempts, approximates the spectral graph convolutions using …

The prediction of a Lithium-ion battery's lifetime is very important for ensuring safety and
reliability. In addition, it is utilized as an early warning system to prevent the battery's failure …

In recent years, traffic congestion prediction has led to a growing research area, especially
of machine learning of artificial intelligence (AI). With the introduction of big data by …

Abstract Short-term Electric Vehicle (EV) charging demand prediction is an essential task in
the fields of smart grid and intelligent transportation systems, as understanding the …

Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart
cities. Travelers as well as urban managers rely on reliable traffic information to make their …