8 citations · 9 across the 6 of their papers we have counts for
6 papers · 1 filter
From Observations to States: Latent Time Series Forecasting
Jie Yang, Yifan Hu, Yuante Li +3
Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Latent Chaos: models with accurate pr…
Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection
Jie Yang, Rui Zhang, Ziyang Cheng +3
Nowadays, Graph Fraud Detection (GFD) in financial scenarios has become an urgent research topic to protect online payment security. However, as organized crime groups are becoming…
Glocal Information Bottleneck for Time Series Imputation
Jie Yang, Kexin Zhang, Guibin Zhang +2
Time Series Imputation (TSI), which aims to recover missing values in temporal data, remains a fundamental challenge due to the complex and often high-rate missingness in real-worl…
Revisiting Multivariate Time Series Forecasting with Missing Values
Jie Yang, Yifan Hu, Kexin Zhang +3
Missing values are common in real-world time series, and multivariate time series forecasting with missing values (MTSF-M) has become a crucial area of research for ensuring reliab…
Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou +4
Although contrastive and other representation-learning methods have long been explored in vision and NLP, their adoption in modern time series forecasters remains limited. We belie…
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
Rui Zhang, Dawei Cheng, Xin Liu +4
Graph-based anomaly detection is currently an important research topic in the field of graph neural networks (GNNs). We find that in graph anomaly detection, the homophily distribu…