Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2024
Abstract
Time Series Anomaly Detection (TSAD) finds widespread applications across various domains such as financial markets, industrial production, and healthcare. Its primary objective is to learn the normal patterns of time series data, thereby identifying deviations in test samples. Most existing TSAD methods focus on modeling data from the temporal dimension, while ignoring the semantic information in the spatial dimension. To address this issue, we introduce a novel approach, called Spatial-Temporal Normality learning (STEN). STEN is composed of a sequence Order prediction-based Temporal Normality learning (OTN) module that captures the temporal correlations within sequences, and a Distance prediction-based Spatial Normality learning (DSN) module that learns the relative spatial relations between sequences in a feature space. By synthesizing these two modules, STEN learns expressive spatial-temporal representations for the normal patterns hidden in the time series data. Extensive experiments on five popular TSAD benchmarks show that STEN substantially outperforms state-of-the-art competing methods. Our code is available at https://github.com/mala-lab/ STEN.
Keywords
Anomaly Detection, Time Series, Self-supervised Learning, Normality Learning
Discipline
Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024
Volume
14946
First Page
145
Last Page
162
ISBN
9783031703645
Identifier
10.1007/978-3-031-70365-2\_9
Publisher
Springer
City or Country
Cham
Citation
CHEN, Yutong; XU, Hongzuo; PANG, Guansong; QIAO, Hezhe; ZHOU, Yuan; and SHANG, Mingsheng.
Self-supervised spatial-temporal normality learning for time series anomaly detection. (2024). Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024. 14946, 145-162.
Available at: https://ink.library.smu.edu.sg/sis_research/9874
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/978-3-031-70365-2\_9
