A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
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) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Foundation models for time series analysis: A tutorial and survey
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 …
cornerstone for extracting valuable insights crucial to a myriad of real-world applications …
Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
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 …
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …
Large models for time series and spatio-temporal data: A survey and outlook
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 …
applications. They capture dynamic system measurements and are produced in vast …
UniST: a prompt-empowered universal model for urban spatio-temporal prediction
Urban spatio-temporal prediction is crucial for informed decision-making, such as traffic
management, resource optimization, and emergence response. Despite remarkable …
management, resource optimization, and emergence response. Despite remarkable …
Exgc: Bridging efficiency and explainability in graph condensation
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 …
However, the associated computational and storage overheads raise concerns. In sight of …
Deep learning for cross-domain data fusion in urban computing: Taxonomy, advances, and outlook
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 …
sustainable development by harnessing the power of cross-domain data fusion from diverse …
Towards Dynamic Spatial-Temporal Graph Learning: A Decoupled Perspective
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 …
data is continuously collected through streaming manners, which has propelled the …
Heterogeneity-Informed Meta-Parameter Learning for Spatiotemporal Time Series Forecasting
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 …
applications. While significant progress has been made in this area, fully capturing and …
Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks
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 …
research of dynamic graphs, but are limited by the low transparency, such that human …