TY - GEN
T1 - An Improved Dro-Based Recurrent Neural Networks for Large-Scale Light Curve Time Series Prediction
AU - Lu, Cheng
AU - Peng, Lei
AU - Bi, Jing
AU - Yuan, Haitao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/4/12
Y1 - 2019/4/12
N2 - In light curves, the brightness of stars is associated with time, and it is an image of the brightness with respect to time. The traditional data processing methods cannot effectively handle real-time and large-volume data of various light curves. To address this issue, this work develops a deep neural network, named Dropout Recurrent Neural Networks (DRNN). It extracts complicated characteristics of all images captured by Mini Ground-based Wide-Angle Camera array (Mini-GWAC) for point source extraction and cross-certification through Long Short-Term Memory units. Furthermore, this work optimizes the training model by combining a dropout method, which predicts changes of the star brightness in advance. Extensive experiments with Mini-GWAC dataset demonstrate that DRNN outperforms several typical baseline methods with respective to forecasting performance of star brightness in large-scale astronomical light curves.
AB - In light curves, the brightness of stars is associated with time, and it is an image of the brightness with respect to time. The traditional data processing methods cannot effectively handle real-time and large-volume data of various light curves. To address this issue, this work develops a deep neural network, named Dropout Recurrent Neural Networks (DRNN). It extracts complicated characteristics of all images captured by Mini Ground-based Wide-Angle Camera array (Mini-GWAC) for point source extraction and cross-certification through Long Short-Term Memory units. Furthermore, this work optimizes the training model by combining a dropout method, which predicts changes of the star brightness in advance. Extensive experiments with Mini-GWAC dataset demonstrate that DRNN outperforms several typical baseline methods with respective to forecasting performance of star brightness in large-scale astronomical light curves.
KW - Dropout
KW - Light curve
KW - Recurrent neural networks
KW - Time series forecasting
UR - https://www.scopus.com/pages/publications/85064966223
U2 - 10.1109/CCIS.2018.8691406
DO - 10.1109/CCIS.2018.8691406
M3 - 会议稿件
AN - SCOPUS:85064966223
T3 - Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2018
SP - 117
EP - 121
BT - Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2018
Y2 - 23 November 2018 through 25 November 2018
ER -