3 citations · 5 across the 6 of their papers we have counts for
6 papers
STA-GANN: A Valid and Generalizable Spatio-Temporal Kriging Approach
Yujie Li, Zezhi Shao, Chengqing Yu +6
Spatio-temporal tasks often encounter incomplete data arising from missing or inaccessible sensors, making spatio-temporal kriging crucial for inferring the completely missing temp…
Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Rates
Chengqing Yu, Fei Wang, Chuanguang Yang +6
Multivariate Time Series Forecasting (MTSF) involves predicting future values of multiple interrelated time series. Recently, deep learning-based MTSF models have gained significan…
BLAST: Balanced Sampling Time Series Corpus for Universal Forecasting Models
Zezhi Shao, Yujie Li, Fei Wang +7
The advent of universal time series forecasting models has revolutionized zero-shot forecasting across diverse domains, yet the critical role of data diversity in training these mo…
Context-Enhanced Multi-View Trajectory Representation Learning: Bridging the Gap through Self-Supervised Models
Tangwen Qian, Junhe Li, Yile Chen +4
Modeling trajectory data with generic-purpose dense representations has become a prevalent paradigm for various downstream applications, such as trajectory classification, travel t…
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
Chengqing Yu, Fei Wang, Zezhi Shao +4
Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely forecast the future values/trends, based on the complex relationships identified from histor…
AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction
Tangwen Qian, Yile Chen, Gao Cong +2
Multi-agent trajectory prediction, as a critical task in modeling complex interactions of objects in dynamic systems, has attracted significant research attention in recent years.…