TY - GEN
T1 - Robust anomaly detection for multivariate time series through stochastic recurrent neural network
AU - Su, Ya
AU - Liu, Rong
AU - Zhao, Youjian
AU - Sun, Wei
AU - Niu, Chenhao
AU - Pei, Dan
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/25
Y1 - 2019/7/25
N2 - Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management. However, due to the complex temporal dependence and stochasticity of multivariate time series, their anomaly detection remains a big challenge. This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. Its core idea is to capture the normal patterns of multivariate time series by learning their robust representations with key techniques such as stochastic variable connection and planar normalizing flow, reconstruct input data by the representations, and use the reconstruction probabilities to determine anomalies. Moreover, for a detected entity anomaly, OmniAnomaly can provide interpretations based on the reconstruction probabilities of its constituent univariate time series. The evaluation experiments are conducted on two public datasets from aerospace and a new server machine dataset (collected and released by us) from an Internet company. OmniAnomaly achieves an overall F1-Score of 0.86 in three real-world datasets, significantly outperforming the best performing baseline method by 0.09. The interpretation accuracy for OmniAnomaly is up to 0.89.
AB - Industry devices (i.e., entities) such as server machines, spacecrafts, engines, etc., are typically monitored with multivariate time series, whose anomaly detection is critical for an entity's service quality management. However, due to the complex temporal dependence and stochasticity of multivariate time series, their anomaly detection remains a big challenge. This paper proposes OmniAnomaly, a stochastic recurrent neural network for multivariate time series anomaly detection that works well robustly for various devices. Its core idea is to capture the normal patterns of multivariate time series by learning their robust representations with key techniques such as stochastic variable connection and planar normalizing flow, reconstruct input data by the representations, and use the reconstruction probabilities to determine anomalies. Moreover, for a detected entity anomaly, OmniAnomaly can provide interpretations based on the reconstruction probabilities of its constituent univariate time series. The evaluation experiments are conducted on two public datasets from aerospace and a new server machine dataset (collected and released by us) from an Internet company. OmniAnomaly achieves an overall F1-Score of 0.86 in three real-world datasets, significantly outperforming the best performing baseline method by 0.09. The interpretation accuracy for OmniAnomaly is up to 0.89.
KW - Anomaly Detection
KW - Multivariate Time Series
KW - Recurrent Neural Network
KW - Stochastic Model
UR - http://www.scopus.com/inward/record.url?scp=85071168275&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071168275&partnerID=8YFLogxK
U2 - 10.1145/3292500.3330672
DO - 10.1145/3292500.3330672
M3 - Conference contribution
AN - SCOPUS:85071168275
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 2828
EP - 2837
BT - KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
T2 - 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Y2 - 4 August 2019 through 8 August 2019
ER -