Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Exploring Sequential Algorithms for Anomaly Detection in Multivariate Time Series. / Мусаев, Андрей Александрович; Макшанов, Андрей; Григорьев, Дмитрий Алексеевич.
Proceedings - 2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024. Institute of Electrical and Electronics Engineers Inc., 2024. p. 1050-1055.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
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TY - GEN
T1 - Exploring Sequential Algorithms for Anomaly Detection in Multivariate Time Series
AU - Мусаев, Андрей Александрович
AU - Макшанов, Андрей
AU - Григорьев, Дмитрий Алексеевич
PY - 2024/5/20
Y1 - 2024/5/20
N2 - This study delves into the development and application of sequential algorithms for detecting spontaneous changes, or anomalies, in the probabilistic characteristics of multivariate time series. The research is primarily motivated by the challenges associated with providing mathematical support for decision-making processes that depend on data from multi-channel monitoring of large systems. The focus is on the spatial-temporal dynamics of multidimensional time series measurements. Unlike conventional approaches, this study proposes innovative techniques for examining inter-channel connections. These techniques involve reducing the dimensionality of the data by representing data matrices in terms of their first singular basis and employing multiple regression in the projection space. The paper also demonstrates the practical application of the developed approach in analyzing the characteristics of turbulent flow, based on measurements of pressure deviation at different spatial locations. This research contributes significantly to the field by offering a novel approach to anomaly detection in multivariate time series data.
AB - This study delves into the development and application of sequential algorithms for detecting spontaneous changes, or anomalies, in the probabilistic characteristics of multivariate time series. The research is primarily motivated by the challenges associated with providing mathematical support for decision-making processes that depend on data from multi-channel monitoring of large systems. The focus is on the spatial-temporal dynamics of multidimensional time series measurements. Unlike conventional approaches, this study proposes innovative techniques for examining inter-channel connections. These techniques involve reducing the dimensionality of the data by representing data matrices in terms of their first singular basis and employing multiple regression in the projection space. The paper also demonstrates the practical application of the developed approach in analyzing the characteristics of turbulent flow, based on measurements of pressure deviation at different spatial locations. This research contributes significantly to the field by offering a novel approach to anomaly detection in multivariate time series data.
KW - change detection
KW - correlation analysis
KW - data fusion
KW - detection algorithms
KW - dimensionality reduction
KW - monitoring
KW - singular value decomposition
UR - https://www.mendeley.com/catalogue/721b02d6-0965-3231-88e4-6cfc521607dc/
U2 - 10.1109/icieam60818.2024.10553874
DO - 10.1109/icieam60818.2024.10553874
M3 - Conference contribution
SN - 9798350395013
SP - 1050
EP - 1055
BT - Proceedings - 2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 20 May 2024 through 24 May 2024
ER -
ID: 123947653