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20202022
most citedSpectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

116 citations · 215 across the 6 of their papers we have counts for

collaborators

6 papers

q-fin.ST20221 cited

DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift

Defu Cao, Yousef El-Laham, Loc Trinh +2

In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a g…

cs.LG20226 cited

Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media

Yizhou Zhang, Defu Cao, Yan Liu

Recent years have witnessed the rise of misinformation campaigns that spread specific narratives on social media to manipulate public opinions on different areas, such as politics…

cs.LG202258 cited

When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning

Chuizheng Meng, Sungyong Seo, Defu Cao +2

Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviour…

cs.CV20214 cited

Spectral Temporal Graph Neural Network for Trajectory Prediction

Defu Cao, Jiachen Li, Hengbo Ma +1

An effective understanding of the contextual environment and accurate motion forecasting of surrounding agents is crucial for the development of autonomous vehicles and social mobi…

cs.LG2021116 cited

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting

Defu Cao, Yujing Wang, Juanyong Duan +8

Multivariate time-series forecasting plays a crucial role in many real-world applications. It is a challenging problem as one needs to consider both intra-series temporal correlati…

cs.LG202030 cited

Multivariate Time-series Anomaly Detection via Graph Attention Network

Hang Zhao, Yujing Wang, Juanyong Duan +7

Anomaly detection on multivariate time-series is of great importance in both data mining research and industrial applications. Recent approaches have achieved significant progress…