# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 5 Discussion  
An overview of anomaly detection models in spacecraft telemetry data was presented in this paper. In the existing literature, anomaly detection models for time series data can be categorized based on their main concepts and architectures into two main groups: thresholding methods and AI methods. Anomaly detection based on thresholding methods relies on simple processing and inspection of data by domain experts. However, with the evolution of spacecraft telemetry systems, the volume of data has increased, making such classical thresholding methods impractical. On the other hand, automatic anomaly detection algorithms, including ML methods, are becoming increasingly prevalent. Over the last decades, several ML modelshave been suggested for this purpose. However, the efficiency of these methods often degrades when applied to high data volumes or dimensions [7; 56]. Since they have the capability to handle complex data and extract features from large-scale datasets, DL models have resulted in significant improvements over traditional ML models, making them more appropriate for anomaly detection applications. DL-based models for spacecraft anomaly detection can be broadly classified into three categories. The first category comprises models that utilize CNNs. CNNs are a family of neural networks used to process multi-variate time series data in spacecraft. Processing both spatial and temporal features in the data, CNNs are able to detect the different kind of anomalous patterns and unexpected events in spacecraft systems with high precision. The second group of methods involves RNN models. The RNNs are designed to process sequential data, allowing for the modeling of temporal features within the sequences. The third category encompasses transformers. Transformers have proven to be the best models to process long-term sequences. They have succeeded in effectively decreasing the requirement of computation resources. CNNs and RNNs models perform very well and even better than transformers for short-length series. To leverage the advantages of different thresholding, ML, and DL approaches, some hybrid models have been suggested. So far, this study has exposed the state-of-the-art models as well as emerging trends in anomaly detection application in spacecraft telemetry data. To further boost this area of knowledge, it is necessary to identify and discuss the key challenges in this field. The main challenges affecting the anomaly detection of spacecraft telemetry data are presented below:  
* Detect complex types of anomalies: Spacecraft data missions represent uneven degrees of difficulty when it comes to anomaly detection. It is typically hard to predict complex types of anomalies such as sequential and contextual anomalies with limited training data and a wide variety of patterns. This can explain the lower performance for some missions like the MSL [20; 22; 36] and OPS-SAT [26; 40]. * Small number of public datasets available: For several reasons (in particular confidential ones), most of the studies chose to do not publish the explored dataset or any significant details about them. This prevents being able to compare and assess the different anomaly identification methodologies' performances in a fair way and limits our ability to identify the most optimal method. * Limited availability of labeled anomaly data: The use of semi-supervised or unsupervised methods could be a good solution to solve the problem of lack of ground truth in anomaly detection datasets. However, they still give a lower performance compared to supervised algorithms [40]. * Real time processing: One major challenge in detecting anomalies in spacecraft telemetry data is monitoring channels in near real time to avoid or mitigate a system failure. Early anomaly detection is valuable, but reliable solutions for real-time anomaly detection do not yet exist in the literature.