22 citations · 67 across the 13 of their papers we have counts for
15 papers
AirDDE: Multifactor Neural Delay Differential Equations for Air Quality Forecasting
Binqing Wu, Zongjiang Shang, Shiyu Liu +3
Accurate air quality forecasting is essential for public health and environmental sustainability, but remains challenging due to the complex pollutant dynamics. Existing deep learn…
Multi-scale hypergraph meets LLMs: Aligning large language models for time series analysis
Zongjiang Shang, Dongliang Cui, Binqing Wu +1
Recently, there has been great success in leveraging pre-trained large language models (LLMs) for time series analysis. The core idea lies in effectively aligning the modality betw…
MillGNN: Learning Multi-Scale Lead-Lag Dependencies for Multi-Variate Time Series Forecasting
Binqing Wu, Zongjiang Shang, Jianlong Huang +1
Multi-variate time series (MTS) forecasting is crucial for various applications. Existing methods have shown promising results owing to their strong ability to capture intra- and i…
ST-Hyper: Learning High-Order Dependencies Across Multiple Spatial-Temporal Scales for Multivariate Time Series Forecasting
Binqing Wu, Jianlong Huang, Zongjiang Shang +1
In multivariate time series (MTS) forecasting, many deep learning based methods have been proposed for modeling dependencies at multiple spatial (inter-variate) or temporal (intra-…
Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting
Yingtao Luo, Shikai Fang, Binqing Wu +2
Weather forecasting is essential but remains computationally intensive and physically incomplete in traditional numerical weather prediction (NWP) methods. Deep learning (DL) model…
Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
Zongjiang Shang, Ling Chen, Binqing wu +1
Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individ…
MSHyper: Multi-Scale Hypergraph Transformer for Long-Range Time Series Forecasting
Zongjiang Shang, Ling Chen, Binqing Wu +1
Demystifying interactions between temporal patterns of different scales is fundamental to precise long-range time series forecasting. However, previous works lack the ability to mo…
DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling
Pengwei Liu, Wenwei Wang, Bingqing Peng +2
While widely recognized as one of the most substantial weather forecasting methodologies, Numerical Weather Prediction (NWP) usually suffers from relatively coarse resolution and i…
WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction
Binqing Wu, Weiqi Chen, Wengwei Wang +3
Due to insufficient local area information, numerical weather prediction (NWP) may yield biases for specific areas. Previous studies correct biases mainly by employing handcrafted…
DSTCGCN: Learning Dynamic Spatial-Temporal Cross Dependencies for Traffic Forecasting
Binqing Wu, Ling Chen
Traffic forecasting is essential to intelligent transportation systems, which is challenging due to the complicated spatial and temporal dependencies within a road network. Existin…
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic Forecasting
Juyong Jiang, Binqing Wu, Ling Chen +2
Traffic forecasting is an essential problem in urban planning and computing. The complex dynamic spatial-temporal dependencies among traffic objects (e.g., sensors and road segment…
Multi-Scale Adaptive Graph Neural Network for Multivariate Time Series Forecasting
Ling Chen, Donghui Chen, Zongjiang Shang +4
Multivariate time series (MTS) forecasting plays an important role in the automation and optimization of intelligent applications. It is a challenging task, as we need to consider…
Learning from Multiple Time Series: A Deep Disentangled Approach to Diversified Time Series Forecasting
Ling Chen, Weiqi Chen, Binqing Wu +3
Time series forecasting is a significant problem in many applications, e.g., financial predictions and business optimization. Modern datasets can have multiple correlated time seri…
Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting
Ling Chen, Jiahui Xu, Binqing Wu +4
The problem of air pollution threatens public health. Air quality forecasting can provide the air quality index hours or even days later, which can help the public to prevent air p…
HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
Ling Chen, Jiahui Xu, Binqing Wu +4
Accurately forecasting air quality is critical to protecting general public from lung and heart diseases. This is a challenging task due to the complicated interactions among disti…