Correlation-Based Method for Tracing Multi-dimensional Time Series Data Anomalies

Abstract

This paper proposes a multi-dimensional time series anomaly data detection method based on correlation analysis, to trace the cause of anomaly detection: system failure data and sensor quality problem data are classified, and then real system failures are identified to avoid false detection. Firstly, the time series correlation graph model is proposed, which is further summarized as the time series correlation loop model. The time series correlation set is obtained by extracting the features in the time series correlation cycle, the cause of abnormality is detected, and the system failure is judged according to the result. Through a large number of experiments on real industrial data sets, the effectiveness of the method in the detection of abnormal sources of high-dimensional time series data is verified. Through comparative experiments, it is verified that the method is superior to fundamental algorithms based on statistics and machine learning models in terms of stability and efficiency. The higher dimensionality of time series, the more obvious improvement of the method compared with the fundamental algorithms. This method not only saves the cost, but also realizes the accurate identification of multi-dimensional abnormal data.

Keywords

• multi-dimensional time series
• abnormal detection
• correlation analysis
• graph algorithm
• industrial big data
• provenance