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

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%. Same models are also applied to MIT-BIH Arrhythmia Databases ECG dataset with similar abnormal pattern and we yield from both sets that we can reduce up to 40% of time consuming for researchers to adjust the model to 90% accuracy.

gravitational lensing, recurrent neural networks, ARIMA, time series prediction and alarming

1. . V. Mayer-Schnberger, K. Cukier. Big data: A revolution that will transform how we live, work and think. Houghton Mifflin Harcourt, 2013, 256 p.

2. . Sima Siami-Namini, Akbar Siami Namin, Forecasting Economics and Financial Time Series: ARIMA vs. LSTM, arXiv:1803.06386, 2018, 19 p.

3. . G. Jenkins G.E.P. Box. Time series analysis, forecasting and control. Holden-Day, San Francisco, CA, 1970.

4. . G.P. Zhang. Time series forecasting using a hybrid arima and neural network model. Neurocomputing, vol. 50, 2003, pp. 159-175.

5. MIT-BIH Arrythmia Database: https://physionet.org/physiobank/database/mitdb/