Adaptive PCA-Based Outlier Detection for Multi-Feature Time Series in Space Missions

2025-04-22

AI Summary

To address the challenge of real-time anomaly detection in multi-feature time-series telemetry data under stringent on-board computational and downlink bandwidth constraints in space missions, this paper proposes a lightweight online anomaly identification method. The method employs a sliding window to compute reconstruction errors for generating real-time anomaly scores. Its key contributions are: (1) an incremental Principal Component Analysis (PCA) framework that dynamically adapts to spatial data distribution drift; and (2) a pre-scaling mechanism preserving intra-feature variance, enabling robust, model-free normalization without prior knowledge. Evaluated on NASA’s Magnetospheric Multiscale (MMS) mission data, the method successfully detects transient events on both dayside and nightside magnetospheric regions, as well as plasma transition layers. On THEMIS data, it accurately identifies dayside transients using only on-board observable parameters. Results demonstrate low computational overhead, high adaptability to non-stationary environments, and strong generalization across missions and instrument configurations.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Detects outliers in multi-feature space mission time series data
• Adapts to evolving data distributions without predefined models
• Optimizes for limited onboard computational and downlink resources

Innovation

• Adaptive PCA-based outlier detection algorithm
• Incremental PCA for dynamic data adaptation
• Pre-scaling process for feature normalization