Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions
This work addresses the need for efficient, real-time event detection in space missions with limited computational resources, though it is incremental as it adapts existing PCA methods for specific applications.
The paper tackles the problem of detecting outliers in multi-feature time series data for space missions by developing an adaptive PCA-based algorithm, demonstrating its effectiveness in identifying space plasma events like transients and transition layers using NASA MMS and THEMIS mission data.
Analyzing multi-featured time series data is critical for space missions making efficient event detection, potentially onboard, essential for automatic analysis. However, limited onboard computational resources and data downlink constraints necessitate robust methods for identifying regions of interest in real time. This work presents an adaptive outlier detection algorithm based on the reconstruction error of Principal Component Analysis (PCA) for feature reduction, designed explicitly for space mission applications. The algorithm adapts dynamically to evolving data distributions by using Incremental PCA, enabling deployment without a predefined model for all possible conditions. A pre-scaling process normalizes each feature's magnitude while preserving relative variance within feature types. We demonstrate the algorithm's effectiveness in detecting space plasma events, such as distinct space environments, dayside and nightside transients phenomena, and transition layers through NASA's MMS mission observations. Additionally, we apply the method to NASA's THEMIS data, successfully identifying a dayside transient using onboard-available measurements.