11 citations · 16 across the 9 of their papers we have counts for
5 papers · 1 filter
Latent Laplace Diffusion for Irregular Multivariate Time Series
Zinuo You, Jin Zheng, John Cartlidge
Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, while continuous-time models of…
Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
Yunhua Pei, Zixing Song, Jin Zheng +1
Anomaly detection in multivariate time series (MTS) is hindered by dynamic inter-variable dependencies and feature entanglement under spectral noise, and in practice, is further co…
How Wide and How Deep? Mitigating Over-Squashing of GNNs via Channel Capacity Constrained Estimation
Zinuo You, Jin Zheng, John Cartlidge
Existing graph neural networks typically rely on heuristic choices for hidden dimensions and propagation depths, which often lead to severe information loss during propagation, kno…
VMDNet: Temporal Leakage-Free Variational Mode Decomposition for Electricity Demand Forecasting
Weibin Feng, Ran Tao, John Cartlidge +1
Accurate electricity demand forecasting is challenging due to the strong multi-periodicity of real-world demand series, which makes effective modeling of recurrent temporal pattern…
Dynamic Graph Representation with Contrastive Learning for Financial Market Prediction: Integrating Temporal Evolution and Static Relations
Yunhua Pei, Jin Zheng, John Cartlidge
Temporal Graph Learning (TGL) is crucial for capturing the evolving nature of stock markets. Traditional methods often ignore the interplay between dynamic temporal changes and sta…