文章基本信息

标题：Multiple-Hour-Ahead Forecast of the Dst Index Using a Combination of Long Short-Term Memory Neural Network and Gaussian Process
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作者：M. A. Gruet ; M. Chandorkar ; A. Sicard 等
期刊名称：Space Weather
印刷版ISSN：1542-7390
出版年度：2018
卷号：16
期号：11
页码：1882-1896
DOI：10.1029/2018SW001898
语种：English
出版社：American Geophysical Union
摘要：In this study, we present a method that combines a Long Short-Term Memory (LSTM) recurrent neural network with a Gaussian process (GP) model to provide up to 6-hr-ahead probabilistic forecasts of the Dst geomagnetic index. The proposed approach brings together the sequence modeling capabilities of a recurrent neural network with the error bars and confidence bounds provided by a GP. Our model is trained using the hourly OMNI and Global Positioning System (GPS) databases, both of which are publicly available. We first develop a LSTM network to get a single-point prediction of Dst. This model yields great accuracy in forecasting the Dst index from 1 to 6 hr ahead, with a correlation coefficient always higher than 0.873 and a root-mean-square error lower than 9.86. However, even if global metrics show excellent performance, it remains poor in predicting intense storms (Dst < −250 nT) 6 hr in advance. To improve it and to obtain probabilistic forecasts, we combine the LSTM model obtained with a GP and evaluate the hybrid predictor using the receiver operating characteristic curve and the reliability diagram. We conclude that this hybrid methodology provides improvements in the forecast of geomagnetic storms, from 1 to 6 hr ahead.