4 citations · 7 across the 8 of their papers we have counts for
9 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…
Risk Management with Feature-Enriched Generative Adversarial Networks (FE-GAN)
Ling Chen
This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value…
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…
D-PAD: Deep-Shallow Multi-Frequency Patterns Disentangling for Time Series Forecasting
Xiaobing Yuan, Ling Chen
In time series forecasting, effectively disentangling intricate temporal patterns is crucial. While recent works endeavor to combine decomposition techniques with deep learning, mu…
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…
TPRNN: A Top-Down Pyramidal Recurrent Neural Network for Time Series Forecasting
Ling Chen, Jiahua Cui
Time series refer to a series of data points indexed in time order, which can be found in various fields, e.g., transportation, healthcare, and finance. Accurate time series foreca…