From Chaos to Clarity: Time Series Anomaly Detection in Astronomical
  Observations
Xinli Hao Yile Chen Chen Yang Zhihui Du Chaohong Ma Chao Wu Xiaofeng Meng
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.