Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting
Support vector machine as an efficient tool for high‐dimensional data processing: Application to substorm forecasting
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DOI：
10.1029/2001ja900118
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发表时间：
2001-12
影响因子：
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通讯作者：
V. Gavrishchaka;S. B. Ganguli
中科院分区：
文献类型：
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作者：
V. Gavrishchaka;S. B. Ganguli
支持向量机（SVM）已被用来模拟太阳风驱动的地磁亚暴活动的极光电喷流（AE）指数的特点。本研究的重点，这是第一次应用SVM空间物理问题，是可靠的预测大振幅亚暴事件的太阳风和行星际磁场数据。这个预报问题对于许多实际应用以及进一步了解整个亚暴动力学是很重要的。支持向量机已被训练符号编码的AE指数时间序列进行超临界/亚临界分类相对于一个应用程序相关的阈值。结果表明，支持向量机的性能可以媲美，甚至上级的神经网络模型。基于支持向量机的技术的优势预计在未来的空间气象预报模型中将更加明显，一旦真实的实时提供这种信息在技术上变得可行，这些模型将纳入许多类型的高维、多尺度输入数据。
The support vector machine (SVM) has been used to model solar wind-driven geomagnetic substorm activity characterized by the auroral electrojet (AE) index. The focus of the present study, which is the first application of the SVM to space physics problems, is reliable prediction of large-amplitude substorm events from solar wind and interplanetary magnetic field data. This forecasting problem is important for many practical applications as well as for further understanding of the overall substorm dynamics. SVM has been trained on symbolically encoded AE index time series to perform supercritical/subcritical classification with respect to an application-dependent threshold. It is shown that SVM performance can be comparable to or even superior to that of the neural networks model. The advantages of the SVM-based techniques are expected to be much more pronounced in future space weather forecasting models, which will incorporate many types of high-dimensional, multiscale input data once real time availability of this information becomes technologically feasible.