Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding

• URL: http://arxiv.org/abs/2609.10017v1
• Date: Wed, 09 Sep 2026 10:48:43 GMT
• Title: Structure-Aware Unsupervised Anomaly Detection for Spacecraft Telemetry with Adaptive EVT Thresholding
• Abstract summary: Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods.<n>We propose an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning.
• Score: 2.534247736024776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We propose an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning. The approach combines incremental monthly retraining, statistical model selection, and adaptive Extreme Value Theory (EVT) thresholding for false alarm control. On the ESA Anomalies Dataset (ESA-AD), it achieves $F_{0.5}=0.700$ on Mission~1 and $F_{0.5}=0.698$ on Mission~2 under strict chronological evaluation.

Related papers

• Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study [1.8419890068150178]
  Time-series foundation models (TSFMs) promise reusable representations and zero-shot forecasts.<n>This work presents a protocol-aware empirical assessment across three settings.
  arXiv  Detail & Related papers  (2026-08-24T08:30:06Z)
• Can AI Agents Simulate A/B Test Outcomes? A Validation Framework for Agentic Experimentation [1.4180331276028662]
  A/B testing remains the standard for rolling out new features in the technology industry.<n>Can AI agents---conditioned on behavioral profiles and contextual descriptions of the intervention---simulate outcomes accurately enough to vet candidate treatments before committing live traffic?<n>We formalize this question as a emphSimulated Randomized Controlled Trial (S-RCT) and derive a two-layer error decomposition that separates agent approximation error from subsampling error.
  arXiv  Detail & Related papers  (2026-08-03T14:58:06Z)
• Time-Series Anomaly Classification for Launch Vehicle Propulsion Systems: Fast Statistical Detectors Enhancing LSTM Accuracy and Data Quality [0.0]
  We propose a novel statistical detector based on the Mahalanobis distance and forward-backward detection fractions to adjust the supervised training labels.<n>We demonstrate our method on digital twin simulations of a ground-stage propulsion system with 20.8 minutes of operation per trial and O(108) training timesteps.
  arXiv  Detail & Related papers  (2026-01-07T23:43:27Z)
• Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time [60.341117019125214]
  We propose a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns in graph anomaly detection (GAD)<n>To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level.<n>Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.
  arXiv  Detail & Related papers  (2025-11-10T12:10:05Z)
• Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction [4.113311437158182]
  We present Forecast2Anomaly (F2A), a novel framework that empowers TSFMs with anomaly prediction abilities.<n>First, we propose a joint forecast-anomaly loss that fine-tunes TSFMs to accurately forecast future signals even at anomalous time points.<n>Second, we introduce a Retrieval-Augmented Generation (RAG) module that retrieves historically relevant horizons and conditions predictions on them.
  arXiv  Detail & Related papers  (2025-11-05T03:13:26Z)
• DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment [92.70019102733453]
  Training time-series forecast models requires aligning the conditional distribution of model forecasts with that of the label sequence.<n>We propose DistDF, which achieves alignment by alternatively minimizing a discrepancy between the conditional forecast and label distributions.
  arXiv  Detail & Related papers  (2025-10-28T16:09:59Z)
• Revisiting Multivariate Time Series Forecasting with Missing Values [65.30332997607141]
  Missing values are common in real-world time series.<n>Current approaches have developed an imputation-then-prediction framework that uses imputation modules to fill in missing values, followed by forecasting on the imputed data.<n>This framework overlooks a critical issue: there is no ground truth for the missing values, making the imputation process susceptible to errors that can degrade prediction accuracy.<n>We introduce Consistency-Regularized Information Bottleneck (CRIB), a novel framework built on the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-09-27T20:57:48Z)
• LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Time Series Anomaly Detection [49.52429991848581]
  We propose a Light and Anti-overfitting Retraining Approach (LARA) for deep variational auto-encoder based time series anomaly detection methods (VAEs) This work aims to make three novel contributions: 1) the retraining process is formulated as a convex problem and can converge at a fast rate as well as prevent overfitting; 2) designing a ruminate block, which leverages the historical data without the need to store them; and 3) mathematically proving that when fine-tuning the latent vector and reconstructed data, the linear formations can achieve the least adjusting errors between the ground truths and the fine-tuned ones.
  arXiv  Detail & Related papers  (2023-10-09T12:36:16Z)
• Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers [44.344548601242444]
  We introduce a novel framework, Weakly-supervised RESidual Transformer (WeakREST), to achieve high anomaly detection accuracy.<n>We reformulate the pixel-wise anomaly localization task into a block-wise classification problem.<n>We develop a novel ResMixMatch algorithm, capable of handling the interplay between weak labels and residual-based representations.
  arXiv  Detail & Related papers  (2023-06-06T08:19:30Z)
• Anomalous NO2 emitting ship detection with TROPOMI satellite data and machine learning [0.08602553195689512]
  Starting from 2021, more demanding $textNO_text2$ emission restrictions were introduced for ships operating in the North and Baltic Sea waters. In this study, we present a method for the automated selection of potentially non-compliant ships using a combination of machine learning models on TROPOMI satellite data.
  arXiv  Detail & Related papers  (2023-02-24T16:54:47Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.