From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations

• URL: http://arxiv.org/abs/2403.10220v1
• Date: Fri, 15 Mar 2024 11:39:12 GMT
• Title: From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
• Authors: Xinli Hao, Yile Chen, Chen Yang, Zhihui Du, Chaohong Ma, Chao Wu, Xiaofeng Meng,
• Abstract summary: We propose a two-stage framework for unsupervised anomaly detection in astronomical observations. In the first stage, we employ a Transformer-based encoder-decoder architecture to learn the normal temporal patterns on each star. In the second stage, we enhance the graph neural network with a window-wise graph structure learning to tackle the occurrence of concurrent noise.
• Score: 6.903396830919462
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the development of astronomical facilities, large-scale time series data observed by these facilities is being collected. Analyzing anomalies in these astronomical observations is crucial for uncovering potential celestial events and physical phenomena, thus advancing the scientific research process. However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms. To overcome the challenges, we propose AERO, a novel two-stage framework tailored for unsupervised anomaly detection in astronomical observations. In the first stage, we employ a Transformer-based encoder-decoder architecture to learn the normal temporal patterns on each variate (i.e., star) in alignment with the characteristic of variate independence. In the second stage, we enhance the graph neural network with a window-wise graph structure learning to tackle the occurrence of concurrent noise characterized by spatial and temporal randomness. In this way, AERO is not only capable of distinguishing normal temporal patterns from potential anomalies but also effectively differentiating concurrent noise, thus decreasing the number of false alarms. We conducted extensive experiments on three synthetic datasets and three real-world datasets. The results demonstrate that AERO outperforms the compared baselines. Notably, compared to the state-of-the-art model, AERO improves the F1-score by up to 8.76% and 2.63% on synthetic and real-world datasets respectively.

Related papers

• Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
  We present a machine learning framework that performs complete BNS inference in just one second without making any approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
  arXiv  Detail & Related papers  (2024-07-12T18:00:02Z)
• A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients [0.0]
  Real-time anomaly detection is essential for identifying rare transients in the era of large-scale astronomical surveys. Currently, most anomaly detection algorithms for astronomical transients rely on hand-crafted features extracted from light curves. We introduce an alternative approach to detecting anomalies: using the penultimate layer of a neural network classifier as the latent space for anomaly detection.
  arXiv  Detail & Related papers  (2024-03-21T18:00:00Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• Unraveling the "Anomaly" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution [89.16750999704969]
  Anomaly labels hinder traditional supervised models in time series anomaly detection. Various SOTA deep learning techniques, such as self-supervised learning, have been introduced to tackle this issue. We propose a novel self-supervised learning based Tri-domain Anomaly Detector (TriAD)
  arXiv  Detail & Related papers  (2023-11-19T05:37:18Z)
• Imbalanced Aircraft Data Anomaly Detection [103.01418862972564]
  Anomaly detection in temporal data from sensors under aviation scenarios is a practical but challenging task. We propose a Graphical Temporal Data Analysis framework. It consists three modules, named Series-to-Image (S2I), Cluster-based Resampling Approach using Euclidean Distance (CRD) and Variance-Based Loss (VBL)
  arXiv  Detail & Related papers  (2023-05-17T09:37:07Z)
• AER: Auto-Encoder with Regression for Time Series Anomaly Detection [12.418290128163882]
  Anomaly detection on time series data is increasingly common across various industrial domains. Recent unsupervised machine learning methods have made remarkable progress in tackling this problem. We propose AER (Auto-encoder with Regression), a joint model that combines a vanilla auto-encoder and an LSTM regressor.
  arXiv  Detail & Related papers  (2022-12-27T17:22:21Z)
• Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data [0.0]
  Astronomical transients are stellar objects that become temporarily brighter on various timescales. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients. We present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time.
  arXiv  Detail & Related papers  (2021-12-15T19:02:54Z)
• Real-time detection of anomalies in large-scale transient surveys [0.0]
  We present two novel methods of automatically detecting anomalous transient light curves in real-time. Both methods are based on the simple idea that if the light curves from a known population of transients can be accurately modelled, any deviations from model predictions are likely anomalies.
  arXiv  Detail & Related papers  (2021-10-29T18:29:25Z)
• A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients [1.7152709285783647]
  We present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. Our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies.
  arXiv  Detail & Related papers  (2021-03-22T18:02:19Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)

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.