State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands.
The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. Euclid's success depends on accurate monitoring and interpretation of housekeeping telemetry and science-derived data. Thousands of telemetry parameters, monitored as time series, may or may not impact the quality of scientific data. These parameters have complex interdependencies, often due to physical relationships (e.g., proximity of temperature sensors). Optimising science operations requires careful anomaly detection and identification of hidden parameter states. Moreover, understanding the interactions between known anomalies and physical quantities is crucial yet complex, as related parameters may display anomalies with varied timing and intensity.
We address these challenges by analysing temperature anomalies in Euclid's telemetry from February to August 2024, focusing on eleven temperature parameters and 35 covariates. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions. A second XGBoost model predicts anomalies from covariates, capturing their relationships to temperature anomalies. We identify the top three anomalies per parameter and analyse their interactions with covariates using SHAP (Shapley Additive Explanations), enabling rapid, automated analysis of complex parameter relationships.
Our method demonstrates how machine learning can enhance telemetry monitoring, offering scalable solutions for other missions with similar data challenges.
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
Department of Artificial Intelligence, Kyung Hee University, Yongin, Republic of Korea（韩国成均馆大学人工智能系）；Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea（韩国大学人工智能系）
RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications（计算机学院（国家级试点软件工程学院），北京邮电大学）；China Telecom Research Institute Beijing（中国电信研究院北京分院）；Department of Computer Science, Missouri University of Science and Technology（计算机学院，密苏里科技大学）
Monte Carlo EM for Deep Time Series Anomaly Detection
AWS AI Labs（AWS 人工智能实验室）；Technical University of Munich（慕尼黑工业大学）
时间序列数据经常被离群值或其他类型的异常所破坏。识别异常点本身可以是一个目标（异常检测），也可以是提高其他时间序列任务（例如预测）性能的一种手段。最近基于深度学习的异常检测和预测方法通常假设训练数据中的异常比例小到足以忽略，并将未标记的数据视为来自标称数据分布。我们提出了一个简单而有效的技术，用于增强现有的时间序列模型，使他们明确占异常的训练数据。通过增加训练数据与潜在的异常指标变量，其分布推断，同时使用Monte Carlo EM训练底层模型，我们的方法同时推断异常点，同时提高模型性能的标称数据。我们证明了该方法的有效性，结合它与一个简单的前馈预测模型。我们调查如何异常的训练集影响训练的预测模型，这是常用的时间序列异常检测，并表明我们的方法提高了训练的模型。
Large Language Models for Forecasting and Anomaly Detection: A Systematic Literature Review
AT&T Center for Virtualization（AT&T虚拟化中心）；Southern Methodist University（南卫理公会大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Ohio State University（俄亥俄州立大学）；Universidad Internacional Isabel I de Castilla（卡斯蒂利亚伊莎贝尔一世国际大学）；University of Florida（佛罗里达大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Amazon Sustainability Science and Innovation（亚马逊可持续性科学与创新部门）
Uncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion
Robotics Institute, Athena Research and Innovation Center, Greece（阿提卡研究与创新中心机器人研究所，希腊）；School of ECE, National Technical University of Athens, Greece（雅典技术大学电子工程学院，希腊）；HERON - Hellenic Robotics Center of Excellence, Athens, Greece（希腊卓越机器人中心，雅典，希腊）
Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
Department of Industrial & Management Engineering, Korea University（韩国大学工业与管理工程系）；Department of Industrial Engineering, Seoul National University（首尔国立大学工业工程系）；LG CNS
ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset
Bitdefender；University of Bucharest（布加勒斯特大学）；University of Stuttgart（斯图加特大学）；International Max Planck Research School for Intelligent Systems（智能系统国际马克斯·普朗克研究学校）
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Griffith University（格里菲斯大学）；Monash University（莫纳什大学）；Squirrel AI（松鼠AI）；IDSIA（瑞士IDSIA人工智能实验室）；Università della Svizzera italiana（意大利语瑞士大学）；Politecnico di Milano（米兰理工大学）；The Chinese University of Hong Kong（香港中文大学）
MamNet: A Novel Hybrid Model for Time-Series Forecasting and Frequency Pattern Analysis in Network Traffic
Columbia University（哥伦比亚大学）；University of California, Irvine（加利福尼亚大学欧文分校）；Washington University in St. Louis（圣路易斯华盛顿大学）；University of Southern California（南加利福尼亚大学）；Meta Platform Inc.（Meta平台公司）；Northeastern University（东北大学）
