@inproceedings{e4c2d3c6f647465a91a42c1cc295965a,
title = "An Effective Constraint-Based Anomaly Detection Approach on Multivariate Time Series",
abstract = "With the development of IoT, various sensors are deployed in industry applications. Sensors produce multivariate time series, while error data and abnormal values often exist in the data. Correlation in multivariate time series can be used to identify such anomaly. In this paper, we propose an efficient method to utilize the correlation between multivariate time series with constraint-based anomaly detection. We develop a DP algorithm to execute the detection process, and optimize the algorithm efficiency with 2D range tree. Experiments on real IIoT dataset demonstrate the superiority of our proposed method compared to the prediction based models.",
keywords = "Anomaly detection, Data cleaning, Multivariate time series, Temporal data analysis",
author = "Zijue Li and Xiaoou Ding and Hongzhi Wang",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 4th Asia-Pacific Web and Web-Age Information Management, Joint Conference on Web and Big Data, APWeb-WAIM 2020 ; Conference date: 18-09-2020 Through 20-09-2020",
year = "2020",
doi = "10.1007/978-3-030-60290-1\_5",
language = "英语",
isbn = "9783030602895",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "61--69",
editor = "Xin Wang and Rui Zhang and Young-Koo Lee and Le Sun and Yang-Sae Moon",
booktitle = "Web and Big Data - 4th International Joint Conference, APWeb-WAIM 2020, Proceedings",
address = "德国",
}