4 citations · 6 across the 5 of their papers we have counts for
5 papers
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction
Zichen Wang, Hao Miao, Senzhang Wang +3
Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving. Although considerable research efforts have been…
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
Ronghui Xu, Hao Miao, Senzhang Wang +2
With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. I…
Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
S. Zhang, S. Wang, H. Miao +3
Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining task…
Deep Multi-View Channel-Wise Spatio-Temporal Network for Traffic Flow Prediction
Hao Miao, Senzhang Wang, Meiyue Zhang +3
Accurately forecasting traffic flows is critically important to many real applications including public safety and intelligent transportation systems. The challenges of this proble…
AutoSTL: Automated Spatio-Temporal Multi-Task Learning
Zijian Zhang, Xiangyu Zhao, Hao Miao +3
Spatio-Temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integra…