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 …
A survey on generative diffusion models
Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …
capturing and generalizing patterns within data, we have entered the epoch of all …
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 …
Deciphering spatio-temporal graph forecasting: A causal lens and treatment
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 …
applications. Spatio-Temporal Graph Neural Networks have emerged as the most popular …
Self-supervised learning for time series analysis: Taxonomy, progress, and prospects
Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …
Difftraj: Generating gps trajectory with diffusion probabilistic model
Pervasive integration of GPS-enabled devices and data acquisition technologies has led to
an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal …
an exponential increase in GPS trajectory data, fostering advancements in spatial-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 …
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 …