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…
Graph Concept Bottleneck Models
Haotian Xu, Tsui-Wei Weng, Lam M. Nguyen +1
Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts and allow intervention with concepts to adjust final predictions. Existi…
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…
Hyperbolic Large Language Models
Sarang Patil, Zeyong Zhang, Yiran Huang +2
Large language models (LLMs) have achieved remarkable success and demonstrated superior performance across various tasks, including natural language processing (NLP), weather forec…
Abstracted Shapes as Tokens -- A Generalizable and Interpretable Model for Time-series Classification
Yunshi Wen, Tengfei Ma, Tsui-Wei Weng +2
In time-series analysis, many recent works seek to provide a unified view and representation for time-series across multiple domains, leading to the development of foundation model…