A Real-Time Anomaly Detection in Satellite Telemetry Data Using Artificial Intelligence Techniques Depending on Time-Series Analysis | ||||
| Journal of the ACS Advances in Computer Science | ||||
| Article 2, Volume 14, Issue 1, 2023 PDF (2.26 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/asc.2023.171575.1011 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Mohamed Hussein 
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| High Institute for Computers and Information Technology AL-Shorouk Academy | ||||
| Abstract | ||||
| To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the Auto-regressive integrated moving average (ARIMA), Prophet, Long Short Term Memory (LSTM), and Auto-encoder algorithms were proposed in this paper. The proposed model is used to find anomalous events by comparing the actual observed values with the predicted intervals of telemetry data. First, preprocessing for the raw telemetry data were Handled for the missing values using linear interpolation. Second, Down-casting to reduce the memory storage. Based on this symbolization, the pseudo-period of the data was extracted. Third, the Data Transformation and Scaling to normalize the data within a particular range to helps in speeding up the calculations were applied. Finally, the experimental results for the Prophet model show predictions with high efficiency, stable when detecting anomalies, and requires little computational time. The results of Prophet compared with other applied algorithms, demonstrate the effectiveness and superiority of the proposed model. | ||||
| Keywords | ||||
| Deep learning; Long Short Term Memory (LSTM); Auto-encoder | ||||
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