Time-aware efficient prediction and anomaly detection for large-scale light curves

Abstract

In the era of data explosion, how to process large-scale data is one of the most important problems. This work focuses on the processing of large-scale astronomical data. In the field of astronomy, stellar brightness is an important attribute of the stars. Ground-based wide-angle camera array (GWAC) can provide a huge volume of data for the brightness analysis of numerous stars. Based on the GWAC data, this work aims to analyse and predict the light curves, as well as to conduct early detection of the abnormal variation in brightness of stars for the special astronomical phenomena. To reduce the data processing time, this work proposes a parallel auto-regressive integrated moving average (PARIMA) model to process the mini-GWAC data. After determining the parameters, the model is used to predict the abnormal phenomena. Furthermore, the simulation experiment shows that the proposed PARIMA method can accurately predict and alarm in time.