Research Article
Anomaly Detection Method Based on Granger Causality Modeling
@INPROCEEDINGS{10.1007/978-3-030-69072-4_12, author={Siya Chen and G. Jin and Sun Peng and Lulu Zhang}, title={Anomaly Detection Method Based on Granger Causality Modeling}, proceedings={Wireless and Satellite Systems. 11th EAI International Conference, WiSATS 2020, Nanjing, China, September 17-18, 2020, Proceedings, Part II}, proceedings_a={WISATS PART 2}, year={2021}, month={2}, keywords={Granger analysis Causal relationship Anomaly detection Satellites}, doi={10.1007/978-3-030-69072-4_12} }- Siya Chen
G. Jin
Sun Peng
Lulu Zhang
Year: 2021
Anomaly Detection Method Based on Granger Causality Modeling
WISATS PART 2
Springer
DOI: 10.1007/978-3-030-69072-4_12
Abstract
Satellites are very expensive to manufacture and require high reliability. Monitoring a large amount of telemetry data during the satellite orbit operation, the telemetry data are an important data source for analyzing the internal correlation of the satellite system and detecting anomalies. Telemetry data is in the form of time series, and there may be mutual influence and correlation between these time series. Due to the diversity of its influence and association forms, it is necessary to establish an effective model to determine the association relationship between them in order to detect anomalies on this basis and identify the cause of anomalies. In this paper, we use Granger causality model to analyze correlation between time series of telemetry data and establish a causality model. Detecting anomalies according to the causality which under normal circumstances and find out the cause of the anomalies. The case study shows that our method is effective.
