Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

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 …

Multivariate time series forecasting focuses on predicting future values based on historical
context. State-of-the-art sequence-to-sequence models rely on neural attention between …

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 …

Abstract Graph Neural Network (GNN) models have been extensively researched and
utilised for extracting valuable insights from graph data. The performance of fairness …

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

Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …

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

Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air
pollution from road traffic, is a challenging representation learning task due to the lack of …

Traffic prediction is a keystone for building smart cities in the new era and has found wide
applications in traffic scheduling and management, environment policy making, public …