Multivariate Variance-Based Genetic Ensemble Learning for Satellite Anomaly Detection
Bibliographic record
Abstract
Proactive diagnosis of spacecraft issues and response to conceivable hazards has attracted considerable interest. Hidden anomalies in satellites can cause overall system degradation. In multivariate time series anomaly detection (AD), several sensors report to a fusion center at each timestamp. Each of these sensors measures a specific feature of the satellites. In this paper, we first leverage the eigenvalues of covariance metrics of a multivariate time series to determine the anomaly score at each timestamp. Then, the anomaly scores are fed to various machine learning models. After that, we employ a Multivariate Variance-based Genetic Ensemble (MVVGE) learning method to ensemble the results of several models based on their corresponding performance. More specifically, we ensemble different Neural Network (NN), Random Forest (RF), and Linear Regression (LR) models based on their associated variance to catch the anomalies of three datasets, i.e., two satellite datasets: i) Soil Moisture Active Passive (SMAP), ii) Mars Science Laboratory rover (MSL), and Server Machine Dataset (SMD). Given that establishing the model confidence region is the bottleneck of our approach, we use an approximated version of the Bayesian NNs (BNN)s for acquiring confidence intervals of NN-based models. For RF and LR confidence intervals, we use an empirical method and bootstrapping, respectively. Simulation results confirm the superiority of our proposed approach compared to other methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".