5 papers
CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistency
Xin Wang, Yunshi Wen, Yanan He +4
The operational integrity of complex industrial systems relies on precise anomaly detection and diagnosis. The vast majority of existing methods narrowly focus on capturing tempora…
Regularized Estimation of the Loading Matrix in Factor Models for High-Dimensional Time Series
Xialu Liu, Xin Wang
High-dimensional data analysis using traditional models suffers from overparameterization. Two types of techniques are commonly used to reduce the number of parameters - regulariza…
SC-JEPA: Stabilizing Latent Predictive Learning for Time-Series Anomaly Prediction
Yanan He, Yunshi Wen, Xin Wang +1
Time-series anomaly prediction aims to forecast future system failures before they fully emerge, making latent predictive models such as JEPA a promising framework for capturing pr…
Sparse-Group Factor Analysis for High-Dimensional Time Series
Xin Wang, Xialu Liu
Factor analysis is a widely used technique for dimension reduction in high-dimensional data. However, a key challenge in factor models lies in the interpretability of the latent fa…
Prediction of high-frequency futures return directions based on the mean uncertainty classification methods: An application in China's future market
Ying Peng, Yifan Zhang, Xin Wang
In this paper, we mainly focus on the prediction of short-term average return directions in China's high-frequency futures market. As minor fluctuations with limited amplitude and…