Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
Autoencoder and Kernel Density Estimation Based Approach for Time Series Anomaly Detection. / Аржа, Антон Насерович; Корхов, Владимир Владиславович.
Computational Science and Its Applications – ICCSA 2025 Workshops. 2025. стр. 249–263 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Том 15894).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › Рецензирование
}
TY - GEN
T1 - Autoencoder and Kernel Density Estimation Based Approach for Time Series Anomaly Detection
AU - Аржа, Антон Насерович
AU - Корхов, Владимир Владиславович
PY - 2025/6/28
Y1 - 2025/6/28
N2 - The detection of anomalies in time series is a critical task across various domains including time series generated by a diversity of distributed systems. Despite extensive research, no single algorithm is the undisputed leader in all respects. This paper introduces KDEAE, a novel unsupervised approach for univariate time series anomaly detection. This method combines autoencoders (AE) and kernel density estimation (KDE). The proposed approach segments time series into non-overlapping segments, encodes them using convolutional AE and constructs anomaly scores based on their density obtained from KDE. The method is evaluated on GutenTAG dataset collection using TimeEval framework and compared with 25 algorithms of the same category. The results show that KDEAE outperform 40% algorithms by ROC AUC and 60% by PR AUC scores and 60% by runtime. The paper concludes with suggestions for further research to enhance the method's performance and extend it to multivariate time series.
AB - The detection of anomalies in time series is a critical task across various domains including time series generated by a diversity of distributed systems. Despite extensive research, no single algorithm is the undisputed leader in all respects. This paper introduces KDEAE, a novel unsupervised approach for univariate time series anomaly detection. This method combines autoencoders (AE) and kernel density estimation (KDE). The proposed approach segments time series into non-overlapping segments, encodes them using convolutional AE and constructs anomaly scores based on their density obtained from KDE. The method is evaluated on GutenTAG dataset collection using TimeEval framework and compared with 25 algorithms of the same category. The results show that KDEAE outperform 40% algorithms by ROC AUC and 60% by PR AUC scores and 60% by runtime. The paper concludes with suggestions for further research to enhance the method's performance and extend it to multivariate time series.
KW - Anomaly Detection
KW - Autoencoders
KW - Kernel Density Estimation
KW - Time Series
UR - https://www.mendeley.com/catalogue/9292d4e6-8222-3c64-bd2d-48b15a173e85/
U2 - 10.1007/978-3-031-97648-3_17
DO - 10.1007/978-3-031-97648-3_17
M3 - Conference contribution
SN - 9783031976476
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 249
EP - 263
BT - Computational Science and Its Applications – ICCSA 2025 Workshops
T2 - Computational Science and Its Applications – ICCSA 2025 Workshops
Y2 - 30 June 2025 through 3 July 2025
ER -
ID: 138833380