6 citations · 10 across the 13 of their papers we have counts for
8 papers · 1 filter
Causal Time Series Generation via Diffusion Models
Yutong Xia, Chang Xu, Yuxuan Liang +4
Time series generation (TSG) synthesizes realistic sequences and has achieved remarkable success. Among TSG, conditional models generate sequences given observed covariates, howeve…
CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series
Yutong Xia, Yingying Zhang, Yuxuan Liang +3
Time series anomaly detection has garnered considerable attention across diverse domains. While existing methods often fail to capture the underlying mechanisms behind anomaly gene…
JointDistill: Adaptive Multi-Task Distillation for Joint Depth Estimation and Scene Segmentation
Tiancong Cheng, Ying Zhang, Yuxuan Liang +3
Depth estimation and scene segmentation are two important tasks in intelligent transportation systems. A joint modeling of these two tasks will reduce the requirement for both the…
Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Xu Liu, Juncheng Liu, Gerald Woo +7
Time series foundation models have demonstrated impressive performance as zero-shot forecasters. However, achieving effectively unified training on time series remains an open chal…
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts
Kun Wang, Hao Wu, Guibin Zhang +5
In this paper, we address the issue of modeling and estimating changes in the state of the spatio-temporal dynamical systems based on a sequence of observations like video frames.…
Through the Dual-Prism: A Spectral Perspective on Graph Data Augmentation for Graph Classification
Yutong Xia, Runpeng Yu, Yuxuan Liang +3
Graph Neural Networks have become the preferred tool to process graph data, with their efficacy being boosted through graph data augmentation techniques. Despite the evolution of a…
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning
Junfeng Hu, Xu Liu, Zhencheng Fan +2
Spatio-temporal graph learning is a fundamental problem in modern urban systems. Existing approaches tackle different tasks independently, tailoring their models to unique task cha…
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
Xu Liu, Junfeng Hu, Yuan Li +4
Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific…