94 citations · 174 across the 11 of their papers we have counts for
11 papers · 1 filter
Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud
Rongkun Cui, Nana Zhang, Kun Zhu +1
Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users…
SEMPO: Lightweight Foundation Models for Time Series Forecasting
Hui He, Kun Yi, Yuanchi Ma +3
The recent boom of large pre-trained models witnesses remarkable success in developing foundation models (FMs) for time series forecasting. Despite impressive performance across di…
Amplifier: Bringing Attention to Neglected Low-Energy Components in Time Series Forecasting
Jingru Fei, Kun Yi, Wei Fan +2
We propose an energy amplification technique to address the issue that existing models easily overlook low-energy components in time series forecasting. This technique comprises an…
FilterNet: Harnessing Frequency Filters for Time Series Forecasting
Kun Yi, Jingru Fei, Qi Zhang +4
While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting. Howe…
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…
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…
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
Zhangkai Wu, Longbing Cao, Qi Zhang +2
Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detectio…
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
Wei Fan, Shun Zheng, Pengyang Wang +5
Due to the non-stationarity of time series, the distribution shift problem largely hinders the performance of time series forecasting. Existing solutions either rely on using certa…
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…
Frequency-domain MLPs are More Effective Learners in Time Series Forecasting
Kun Yi, Qi Zhang, Wei Fan +7
Time series forecasting has played the key role in different industrial, including finance, traffic, energy, and healthcare domains. While existing literatures have designed many s…