Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …

With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

Abstract Spatio-Temporal Graph (STG) forecasting is a fundamental task in many real-world
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …

Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …

The impact of air pollution on public health is substantial, and accurate long-term predictions
of air quality are crucial for early warning systems to address this issue. Air quality prediction …

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for
sustainable development by harnessing the power of cross-domain data fusion from diverse …

Spatio-temporal graph neural networks (STGNN) have emerged as the dominant model for
spatio-temporal graph (STG) forecasting. Despite their success, they fail to model intrinsic …

This paper shows that time series forecasting Transformer (TSFT) suffers from severe over-
fitting problem caused by improper initialization method of unknown decoder inputs …

Recent studies have demonstrated the great power of Transformer models for time series
forecasting. One of the key elements that lead to the transformer's success is the channel …

Spatial-Temporal Graph (STG) data is characterized as dynamic, heterogenous, and non-
stationary, leading to the continuous challenge of spatial-temporal graph learning. In the …