TY - JOUR
T1 - Clustering-Based Granular Representation of Time Series With Application to Collective Anomaly Detection
AU - Shi, Wen
AU - Karastoyanova, Dimka
AU - Ma, Yongsheng
AU - Huang, Yongming
AU - Zhang, Guobao
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2023/10/18
Y1 - 2023/10/18
N2 - Efficient anomaly detection is at the forefront of ensuring optimized operations and system safety, especially in the field of engineering. It is deemed indispensable for the enhancement of work efficiency, maximization of resource utilization, and proactive address of potential risks. From this practical perspective, a new method for granular representation of time-series data is proposed in this study and is validated through a collective anomaly detection task. The density peak clustering (DPC) algorithm is employed to evaluate clusters in 2-D data, which are created from the original dataset and its variations. Using the principle of justifiable granularity, each cluster is depicted, allowing the time series to be converted into rectangular information granules. A distinctive similarity measure, designed to assess the similarity among subsequences in this format, is then introduced, with subsequences having reduced similarity being pinpointed as anomalies. Extensive testing on various datasets confirms that the effectiveness and applicability of the proposed method are evident in real-world scenarios. This approach not only enhances the understanding of time-series patterns, but is also recognized for its prominence in engineering applications where precise anomaly detection is demanded. Due to its ability to capture the inherent structure of time series and analyze the correlation among data, superior effectiveness is consistently exhibited by the proposed approach when compared to other existing methods.
AB - Efficient anomaly detection is at the forefront of ensuring optimized operations and system safety, especially in the field of engineering. It is deemed indispensable for the enhancement of work efficiency, maximization of resource utilization, and proactive address of potential risks. From this practical perspective, a new method for granular representation of time-series data is proposed in this study and is validated through a collective anomaly detection task. The density peak clustering (DPC) algorithm is employed to evaluate clusters in 2-D data, which are created from the original dataset and its variations. Using the principle of justifiable granularity, each cluster is depicted, allowing the time series to be converted into rectangular information granules. A distinctive similarity measure, designed to assess the similarity among subsequences in this format, is then introduced, with subsequences having reduced similarity being pinpointed as anomalies. Extensive testing on various datasets confirms that the effectiveness and applicability of the proposed method are evident in real-world scenarios. This approach not only enhances the understanding of time-series patterns, but is also recognized for its prominence in engineering applications where precise anomaly detection is demanded. Due to its ability to capture the inherent structure of time series and analyze the correlation among data, superior effectiveness is consistently exhibited by the proposed approach when compared to other existing methods.
KW - Anomaly detection
KW - granular representation
KW - principle of justifiable granularity
KW - similarity measure
KW - time-series data
UR - http://www.scopus.com/inward/record.url?scp=85174798888&partnerID=8YFLogxK
U2 - 10.1109/TIM.2023.3325521
DO - 10.1109/TIM.2023.3325521
M3 - Article
AN - SCOPUS:85174798888
SN - 0018-9456
VL - 72
JO - IEEE Transactions on Instrumentation and Measurement
JF - IEEE Transactions on Instrumentation and Measurement
M1 - 2530612
ER -