Local Anomaly Detection for Multivariate Time Series by Temporal Dependency Based on Poisson Model

Multivariate time series data are invasive in different domains, ranging from data center supervision and e-commerce data to financial transactions. This kind of data presents an important challenge for anomaly detection due to the temporal dependency aspect of its observations. In this article, we...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 33; no. 11; pp. 6701 - 6711
Main Authors: Benkabou, Seif-Eddine, Benabdeslem, Khalid, Kraus, Vivien, Bourhis, Kilian, Canitia, Bruno
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.11.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Anomalies
Anomaly detection
Computer Science
Data centers
Electronic commerce
Knowledge and data engineering tools and techniques
machine learning
Mathematics
Minimization
Modelling
Multivariate analysis
optimization
Probabilistic logic
Sparse matrices
Task analysis
Time series
Time series analysis
Transmission line matrix methods
Transmission line matrix methods
Time series analysis
Minimization
Probabilistic logic
Sparse matrices
Task analysis
Anomaly detection
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:Multivariate time series data are invasive in different domains, ranging from data center supervision and e-commerce data to financial transactions. This kind of data presents an important challenge for anomaly detection due to the temporal dependency aspect of its observations. In this article, we investigate the problem of unsupervised local anomaly detection in multivariate time series data from temporal modeling and residual analysis perspectives. The residual analysis has been shown to be effective in classical anomaly detection problems. However, it is a nontrivial task in multivariate time series as the temporal dependency between the time series observations complicates the residual modeling process. Methodologically, we propose a unified learning framework to characterize the residuals and their coherence with the temporal aspect of the whole multivariate time series. Experiments on real-world datasets are provided showing the effectiveness of the proposed algorithm.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3083183