94 citations · 160 across the 8 of their papers we have counts for
8 papers
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,…
Deep Frequency Derivative Learning for Non-stationary Time Series Forecasting
Wei Fan, Kun Yi, Hangting Ye +3
While most time series are non-stationary, it is inevitable for models to face the distribution shift issue in time series forecasting. Existing solutions manipulate statistical me…
MLIP: Efficient Multi-Perspective Language-Image Pretraining with Exhaustive Data Utilization
Yu Zhang, Qi Zhang, Zixuan Gong +9
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success, leading to rapid advancements in multimodal studies. However, CLIP faces a notable challenge in terms…
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…
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…
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…