BigData’17 Paper: Light Curve Anomaly Detection

Real-Time Anomaly Detection of Short Time-Scale GWAC Survey Light Curves, Tianzhi Feng, Zhihui Du, Yankui Sun, Jianyan Wei, Jing Bi, and Jason Liu. In Proceedings of 6th IEEE International Congress on Big Data, June 2017. [paper]


Ground-based Wide-Angle Camera array (GWAC) is a short time-scale survey telescope that can take images covering a field of view of over 5,000 square degrees every 15 seconds or even shorter. One scientific missions of GWAC is to accurately and quickly detect anomaly astronomical events. For that, a huge amount of data must be handled in real time. In this paper, we propose a new time series analysis model, called DARIMA (or Dynamic Auto-Regressive Integrated Moving Average), to identify the anomaly events that occur in light curves obtained from GWAC as early as possible with high degree of confidence. A major advantage of DARIMA is that it can dynamically adjust its model parameters during the real-time processing of the time series data. We identify the anomaly points based on the weighted prediction result of different time windows to improve accuracy. Experimental results using real survey data show that the DARIMA model can identify the first anomaly point for all light curves. We also evaluate our model with simulated anomaly events of various types embedded in the real time series data. The DARIMA model is able to generate the early warning triggers for all of them. The results from the experiments demonstrate that the proposed DARIMA model is a promising method for real-time anomaly detection of short time-scale GWAC light curves.


author={Tianzhi Feng and Zhihui Du and Yankui Sun and Jianyan Wei and Jing Bi and Jason Liu},
booktitle={2017 IEEE International Congress on Big Data (BigData Congress)}, 
title={Real-Time Anomaly Detection of Short-Time-Scale GWAC Survey Light Curves},