no code implementations • 11 Jan 2024 • Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Haishuai Wang, Khoa T. Phan, Yi-Ping Phoebe Chen, Shirui Pan, Wei Xiang
However, real-world time series data is usually not well-structured, posting significant challenges to existing approaches: (1) The existence of missing values in multivariate time series data along variable and time dimensions hinders the effective modeling of interwoven spatial and temporal dependencies, resulting in important patterns being overlooked during model training; (2) Anomaly scoring with irregularly-sampled observations is less explored, making it difficult to use existing detectors for multivariate series without fully-observed values.
1 code implementation • 17 Jul 2023 • Yu Zheng, Huan Yee Koh, Ming Jin, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen, Wei Xiang
To overcome these limitations, we propose a novel method, correlation-aware spatial-temporal graph learning (termed CST-GL), for time series anomaly detection.
no code implementations • 27 Feb 2023 • Nam H. Chu, Diep N. Nguyen, Dinh Thai Hoang, Khoa T. Phan, Eryk Dutkiewicz, Dusit Niyato, Tao Shu
This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before.
no code implementations • 11 Feb 2022 • Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Shirui Pan, Yi-Ping Phoebe Chen
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
1 code implementation • 23 Aug 2021 • Yu Zheng, Ming Jin, Yixin Liu, Lianhua Chi, Khoa T. Phan, Yi-Ping Phoebe Chen
While the generative attribute regression module allows us to capture the anomalies in the attribute space, the multi-view contrastive learning module can exploit richer structure information from multiple subgraphs, thus abling to capture the anomalies in the structure space, mixing of structure, and attribute information.