Support vector machine as an efficient tool for high-dimensional data processing: Application to substorm forecasting

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.