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 …

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 …

The problem of air pollution threatens public health. Air quality forecasting can provide the
air quality index hours or even days later, which can help the public to prevent air pollution …