@inproceedings{ed89f682112d49e3b5199238f951b776,
title = "An anomaly detection method based on learning of “scores sequence”",
abstract = "Anomaly detection is very important in the field of operation and maintenance (O\&M). However, in O\&M, we find that direct use of the existing anomaly detection algorithms often causes a large number of false positives, and the detection results are not stable. Nothing a data characteristics in O\&M: Many anomalies are often anomalous time periods formed by continuous anomaly points, we propose a novel concept “Scores Sequence” and a method based on learning of Scores Sequence. Our method has less false positives, can detect anomaly timely, and the detection result of our method is very stable. Through comparative experiments with many algorithms and practical industrial application, it proves that our method has good performance and is very suitable for the anomaly detection in O\&M.",
keywords = "Anomaly detection, False positives, Operation and maintenance, Scores sequence, Stability",
author = "Dongsheng Li and Shengfei Shi and Yan Zhang and Hongzhi Wang and Jizhou Luo",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2018.; 4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018 ; Conference date: 21-09-2018 Through 23-09-2018",
year = "2018",
doi = "10.1007/978-981-13-2206-8\_25",
language = "英语",
isbn = "9789811322051",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "296--311",
editor = "Qinglei Zhou and Hongzhi Wang and Wei Xie and Zeguang Lu and Qiguang Miao and Yan Wang",
booktitle = "Data Science - 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018, Proceedings",
address = "德国",
}