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Hybrid Model for Efficient Anomaly Detection in Short-timescale GWAC Light Curves and Similar Datasets
Programming and Computer Software ( IF 0.7 ) Pub Date : 2020-01-14 , DOI: 10.1134/s0361768819080176
Y. Sun , Z. Zhao , X. Ma , Z. Du

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.


中文翻译:

短时间GWAC光曲线和类似数据集的有效异常检测的混合模型

摘要

天空调查期间的早期预警为探测低质量,自由漂浮的行星提供了重要的机会。特别是,为了实时地从高节奏和广域调查中搜索短时微透镜(ML)事件,将ARIMA(自回归综合移动平均值)与LSTM(长时记忆)和GRU(提出了“门控递归单元”递归神经网络(RNN),以监视所有观察到的光曲线并识别早期的ML事件。实验结果表明,该混合模型精度较高,调整参数耗时较少。ARIMA + LSTM和ARIMA + GRU可以分别将精度提高14.5%和13.2%。在异常检测光曲线的情况下,GRU可以以8%的时间减少与LSTM几乎相同的结果。混合模型也被应用于MIT-BIH心律失常数据库ECG数据集,其异常模式与ML相似。来自两个数据集的实验结果表明,混合模型可以节省多达40%的研究人员时间进行模型调整和优化,以达到90%的准确性。
更新日期:2020-01-14
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