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) …

Missing data is an inevitable and ubiquitous problem for traffic data collection in intelligent
transportation systems. Recent research has employed graph neural networks (GNNs) for …

Intelligent Transportation Systems (ITSs) are vital for alleviating traffic congestion and
improving traffic efficiency. Due to the delay of network transmission and failure of detectors …

Modeling multivariate time series as temporal signals over a (possibly dynamic) graph is an
effective representational framework that allows for developing models for time series …

Neural forecasting of spatiotemporal time series drives both research and industrial
innovation in several relevant application domains. Graph neural networks (GNNs) are often …

Trajectory prediction is a crucial undertaking in understanding entity movement or human
behavior from observed sequences. However, current methods often assume that the …

Spatiotemporal data mining plays an important role in air quality monitoring, crowd flow
modeling, and climate forecasting. However, the originally collected spatiotemporal data in …

Abstract Machine learning, with its advances in deep learning has shown great potential in
analyzing time series. In many scenarios, however, additional information that can …

Spatiotemporal graph neural networks have shown to be effective in time series forecasting
applications, achieving better performance than standard univariate predictors in several …

Missing values, which are common in multivariate time series, is most important obstacle
towards the utilization and interpretation of those data. Great efforts have been employed on …