###### Abstract  
Most of the spacecraft telemetry anomaly detection methods based on statistical models suffer from the problems of high false negatives, long time consumption, and poor interpretability. Besides, complex interactions, which may determine the propagation of anomalous mode between telemetry parameters, are often ignored. To discover the complex interaction between spacecraft telemetry parameters and improve the efficiency and accuracy of anomaly detection, we propose an anomaly detection framework based on parametric causality and Double-Criteria Drift Streaming Peaks Over Threshold (DCDSPOT). We propose Normalized Effective Transfer Entropy (NETE) to reduce the error and noise caused by nonstationarity of the data in the calculation of transfer entropy, and then apply NETE to improve the Multivariate Effective Source Selection (MESS) causal inference algorithm to infer parametric causality. We define the Weighted Source Parameter (WSP) of the target parameter to be detected, then DSPOT is employed to set multi-tier thresholds for target parameter and WSP. At last, two criteria are formulated to determine anomalies. Additionally, to cut the time consumption of the DCDSPOT, we apply Probability Weighted Moments (PWM) for parameter estimation of Generalized Pareto Distribution (GPD). Experiments on real satellite telemetry dataset shows that our method has higher recall and F1-score than other commonly used methods, and the running time is also significantly reduced. a +
Footnote †: journal: Medical  
Methods based on statistical models are commonly used in actual spacecraft operation systems. By making distribution assumptions on historical data and establishing statistical models, reasonable thresholds can be set, and the normal range of the data is then determined. Once the points or sequence are out of this range, a warning will be issued. This kind of method is simple to operate and facilitates the implementation by engineers and technicians. However, anomaly detection based on statistical model also has the following three shortcomings:  
1. [label=0.] 2. Thresholds set merely by establishing a statistical model of historical data have poor scalability, and they do not have the ability to cope with sudden failures and changes of working mode;
3. Many types of anomalies will not cause the variables to exceed the thresholds when they occur. The reason for anomalies may be the abnormal fluctuation or the failure propagation effect;
4. Presetting thresholds via statistical model in large-scale sequence is very time-consuming, and it is laborious to adjust thresholds in consideration of different operating conditions. To solve the problems above, many advanced methods have been proposed recently [8; 9; 10; 11]. Among the existing work, Peaks Over Thresholds (POT) [12] achieves the most competitive performance. POT can be used as an automatic thresholding block that can provide strong statistical guarantees and adapt to the changes in data stream. POT does not rely on any prior knowledge or distribution assumptions, and it can automatically set and update thresholds. However, when it is used in telemetry data anomaly detection, the problems of poor interpretability and high false negative rate are still unsolved. As a kind of multivariate time series, telemetry data has complex causalities among its parameters. These causalities have great potential in telemetry data analysis and anomaly detection, helping to study satellite operation mechanisms, and improve detection efficiency and the interpretability of anomaly detection. Aiming at solving the problems of traditional methods as well as taking advantage of causality between parameters, we propose an anomaly detection framework based on the parametric causality and Double-Criteria Drift Streaming Peaks Over Threshold (DCDSPOT) to realize spacecraft telemetry data anomaly detection with interpretability and high detection efficiency and accuracy. The key contributions of this paper are as follows:  
1. [label=0.] 2.