19 citations · 20 across the 5 of their papers we have counts for
5 papers
STGAtt: A Spatial-Temporal Unified Graph Attention Network for Traffic Flow Forecasting
Zhuding Liang, Jianxun Cui, Qingshuang Zeng +3
Accurate and timely traffic flow forecasting is crucial for intelligent transportation systems. This paper presents a novel deep learning model, the Spatial-Temporal Unified Graph…
Generalising Traffic Forecasting to Regions without Traffic Observations
Xinyu Su, Majid Sarvi, Feng Liu +2
Traffic forecasting is essential for intelligent transportation systems. Accurate forecasting relies on continuous observations collected by traffic sensors. However, due to high d…
DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems
Menglin Kong, Muzhou Hou, Shaojie Zhao +3
Click-Through Rate (CTR) prediction is one of the main tasks of the recommendation system, which is conducted by a user for different items to give the recommendation results. Cros…
Balanced Graph Structure Learning for Multivariate Time Series Forecasting
Weijun Chen, Yanze Wang, Chengshuo Du +3
Accurate forecasting of multivariate time series is an extensively studied subject in finance, transportation, and computer science. Fully mining the correlation and causation betw…
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
Leye Wang, Xu Geng, Xiaojuan Ma +2
Spatio-temporal prediction is a key type of tasks in urban computing, e.g., traffic flow and air quality. Adequate data is usually a prerequisite, especially when deep learning is…