Ocean Observing Time Series Anomaly Detection Based on DTW-TRSAX Method

Abstract

Abstract Ocean observing series anomaly detection is an essential part of maritime supervision. Due to the harsh environment, marine observation equipment is relatively easy to damage. Ocean observing series anomaly detection can assist maritime department to find abnormal equipment in a timely manner, rather than costly periodic inspections, which is of great significance for maintaining the safety and reliability of ocean engineering. Given the problems of the random of ocean systems and the lack of labeled data sets, the trend-based symbolic distance and dynamic time warping algorithm (DTW-TRSAX) were proposed for ocean observing time series anomaly detection. Finally, based on the data set issued by the National Ocean Test Site of China and public data set issued by the National Marine Data Center, our method was verified. The results show that the method is reliable for ocean engineering, can work potentially in a real-time way, and will help ocean engineering managers to obtain informed decisions.