activity
20232025
most citedFourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

60 citations · 65 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2025

Distinguished Quantized Guidance for Diffusion-based Sequence Recommendation

Wenyu Mao, Shuchang Liu, Haoyang Liu +3

Diffusion models (DMs) have emerged as promising approaches for sequential recommendation due to their strong ability to model data distributions and generate high-quality items. E…

cs.LG2024

Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift

Hui He, Qi Zhang, Kun Yi +4

The non-stationary nature of real-world Multivariate Time Series (MTS) data presents forecasting models with a formidable challenge of the time-variant distribution of time series,…

cs.MM20242 cited

HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis

Zhuojia Wu, Qi Zhang, Duoqian Miao +3

Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multim…

cs.LG20243 cited

Deep Coupling Network For Multivariate Time Series Forecasting

Kun Yi, Qi Zhang, Hui He +4

Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- a…

cs.LG202360 cited

FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective

Kun Yi, Qi Zhang, Wei Fan +6

Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually…