Hybrid Model for Efficient Anomaly Detection in Short-timescale GWAC Light Curves and Similar Datasets

Abstract

Early warning during sky survey provides a crucial opportunity to detect low-mass, free-floating planets. In particular, to search short-timescale microlensing (ML) events from high-cadence and wide- field survey in real time, a hybrid method which combines ARIMA (Autoregressive Integrated Moving Average) with LSTM (Long-Short Time Memory) and GRU (Gated Recurrent Unit) recurrent neural networks (RNN) is presented to monitor all observed light curves and identify ML events at their early stages. Experimental results show that the hybrid models perform better in accuracy and less time consuming of adjusting parameters. ARIMA+LSTM and ARIMA+GRU can achieve improvement in accuracy by 14.5% and 13.2%, respectively. In the case of abnormal detection of light curves, GRU can achieve almost the same result as LSTM with less time by 8%. The hybrid models are also applied to MIT-BIH Arrhythmia Databases ECG dataset which has the similar abnormal pattern to ML. The experimental results from both data sets show that the hybrid model can save up to 40% of researchers' time in model adjusting and optimization to achieve 90% accuracy.