论文标题
同时使用复发网络预测全局地磁活动
Simultaneously forecasting global geomagnetic activity using Recurrent Networks
论文作者
论文摘要
社会使用的许多系统非常容易受到太空天气事件的影响，例如太阳耀斑和地磁风暴可能会造成灾难性损害。近年来，通过一些代理来预测这些事件，已经出现了许多作品，以向此类系统提供预警，但是这些方法主要集中在特定现象上。我们提出了一种按小时分辨率预测全球太空天气条件的问题的序列学习方法。这种方法通过同时预测几个关键代理，以提前6小时来改善该领域的其他工作。我们证明了对当前最著名的地磁风暴预测指标的改善，并且提前几个小时对持久基线的改善进行了改善。
Many systems used by society are extremely vulnerable to space weather events such as solar flares and geomagnetic storms which could potentially cause catastrophic damage. In recent years, many works have emerged to provide early warning to such systems by forecasting these events through some proxy, but these approaches have largely focused on a specific phenomenon. We present a sequence-to-sequence learning approach to the problem of forecasting global space weather conditions at an hourly resolution. This approach improves upon other work in this field by simultaneously forecasting several key proxies for geomagnetic activity up to 6 hours in advance. We demonstrate an improvement over the best currently known predictor of geomagnetic storms, and an improvement over a persistence baseline several hours in advance.
