Self-adaptive statistical process control for anomaly detection in time series
作者：
Highlights：
• We model anomaly detection as a statistical testing based on fuzzy set theory.
• Detection rate and false alarm rate almost are not affected by different K.
• K optimization is necessary for AUC performance improvement.
• Fuzzification can effectively reduce false alarm rate.
• This approach results in high AUC performance and reduces the detection time.
摘要
•We model anomaly detection as a statistical testing based on fuzzy set theory.•Detection rate and false alarm rate almost are not affected by different K.•K optimization is necessary for AUC performance improvement.•Fuzzification can effectively reduce false alarm rate.•This approach results in high AUC performance and reduces the detection time.
论文关键词：Anomaly detection,Self-adaptive,Statistical process control,Fuzzy set theory,Statistical testing
论文评审过程：Received 5 January 2015, Revised 15 March 2016, Accepted 16 March 2016, Available online 24 March 2016, Version of Record 12 April 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.03.029
