Multi-Scale Deep Convolutional Time Series Anomaly Detection Based on Correlation Matrix

With the proliferation of multivariate time series data in various fields, this paper proposes an anomaly detection method based on modelling the correlation of multivariate time series. Multiscale Deep Convolutional Anomaly Detection Model (MSDCAD) based on a correlation matrix is introduced. It ch...

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Bibliographic Details
Published in2024 9th International Symposium on Computer and Information Processing Technology (ISCIPT) pp. 613 - 617
Main Authors Zheng, Jiuhao, Duan, Jiangyong, Zhang, Ke, Ma, Zhongsong
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.05.2024
Subjects
Anomaly detection
Correlation
Decoding
Deep learning
Encoding
Feature extraction
Location awareness
multivariate time series
Time series analysis
time series correlation
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Summary:With the proliferation of multivariate time series data in various fields, this paper proposes an anomaly detection method based on modelling the correlation of multivariate time series. Multiscale Deep Convolutional Anomaly Detection Model (MSDCAD) based on a correlation matrix is introduced. It characterizes system states at different time steps by constructing multiscale correlation matrices and encodes inter-series correlation information using a deep convolutional encoder. Secondly, temporal patterns are captured using a convolutional long short-term memory network with an attention mechanism, and the correlation matrix is reconstructed through a convolutional decoder. Finally, anomaly detection is conducted by analyzing the residuals between the original and reconstructed correlation matrices. Testing on multiple datasets has proven the effectiveness of the MSDCAD model in detecting anomalies in multidimensional time series.
DOI:10.1109/ISCIPT61983.2024.10672758