基于 Prophet-GMM 的大坝监测数据异常检测算法.

Due to the influence of environment and other factors, there are often abnormal data in dam monitoring data and the detection of abnormal data plays an indispensable role in the normal operation of the dam. However, the accuracy of traditional anomaly detection algorithms for dam monitoring data often fails to meet the requirements. In this paper, an anomaly detection algorithm based on Prophet GMM was proposed. The better fitting performance of Prophet algorithm was used to fit the dam data and the residual sequence was obtained from the fitting data and the actual data. Then, the residual sequence was clustered by GMM algorithm to accurately identify the abnormal value. The test results show that the method proposed in this paper can accurately identify outliers for different types of dam monitoring data. Compared with the traditional detection algorithm, it has significantly improved the detection indicators of precision, recall and accuracy. [ABSTRACT FROM AUTHOR]