Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis
Multivariate Time Series (MTS) analysis is crucial to understanding and managing complex
systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting …
systems, such as traffic and energy systems, and a variety of approaches to MTS forecasting …
Ginar: An end-to-end multivariate time series forecasting model suitable for variable missing
Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely
forecast the future values/trends, based on the complex relationships identified from …
forecast the future values/trends, based on the complex relationships identified from …
Hybrid spatial–temporal graph neural network for traffic forecasting
P Wang, L Feng, Y Zhu, H Wu - Information Fusion, 2025 - Elsevier
Accurate traffic forecasting is the foundation of the intelligent transportation system (ITS).
Among existing methods, researchers utilize deep neural networks to capture spatial …
Among existing methods, researchers utilize deep neural networks to capture spatial …
Locally differentially private graph learning on decentralized social graph
G Zhang, X Cheng, J Pan, Z Lin, Z He - Knowledge-Based Systems, 2024 - Elsevier
In recent years, decentralized social networks have gained increasing attention, where each
client maintains a local view of a social graph. To provide services based on graph learning …
client maintains a local view of a social graph. To provide services based on graph learning …
Trajectory-User Linking via Multi-Scale Graph Attention Network
Abstract Trajectory-User Linking (TUL) aims to link anonymous trajectories to their owners,
which is considered an essential task in discovering human mobility patterns. Although …
which is considered an essential task in discovering human mobility patterns. Although …
SDA-GRIN for Adaptive Spatial-Temporal Multivariate Time Series Imputation
In various applications, the multivariate time series often suffers from missing data. This
issue can significantly disrupt systems that rely on the data. Spatial and temporal …
issue can significantly disrupt systems that rely on the data. Spatial and temporal …