Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Time series analysis stands as a focal point within the data mining community, serving as a
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …

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 …

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 …

Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …

Graph representation learning on vast datasets, like web data, has made significant strides.
However, the associated computational and storage overheads raise concerns. In sight of …

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 …

With the progress of urban transportation systems, a significant amount of high-quality traffic
data is continuously collected through streaming manners, which has propelled the …

Spatiotemporal time series forecasting plays a key role in a wide range of real-world
applications. While significant progress has been made in this area, fully capturing and …

Dynamic Graph Neural Networks (DyGNNs) have gained significant popularity in the
research of dynamic graphs, but are limited by the low transparency, such that human …