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

Authors: Jing Bi; Tianzhi Feng; Haitao Yuan; Zhen Wei

Addresses: Faculty of Information Technology, School of Software Engineering, Beijing University of Technology, Beijing 100124, China ' Faculty of Information Technology, School of Software Engineering, Beijing University of Technology, Beijing 100124, China ' School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China ' Beijing Complex Product Advanced Manufacturing Engineering Research Center, Beijing Simulation Center, Beijing, China; State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing, China; Science and Technology on Space System Simulation Laboratory, Beijing Simulation Center, Beijing, China

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.

Keywords: PARIMA; real-time analysis; big data processing; large-scale light curves; anomaly detection; time-aware prediction; time series analysis; machine learning; astronomical phenomena; multi-process mechanism; machine intelligence; sensory signal processing; cloud computing; optimisation; artificial intelligence.

DOI: 10.1504/IJMISSP.2018.092935

International Journal of Machine Intelligence and Sensory Signal Processing, 2018 Vol.2 No.2, pp.173 - 188

Received: 19 Apr 2017
Accepted: 28 Nov 2017
Published online: 03 Jul 2018 *

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