5 papers
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-…
Deep Identification of Propagation Trees
Zeeshan Memon, Chen Ling, Ruochen Kong +3
Understanding propagation structures in graph diffusion processes, such as epidemic spread or misinformation diffusion, is a fundamental yet challenging problem. While existing met…
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…