TITLE:
A Nonlinear Autoregressive Approach to Statistical Prediction of Disturbance Storm Time Geomagnetic Fluctuations Using Solar Data
AUTHORS:
Joseph M. Caswell
KEYWORDS:
Space Weather, Geomagnetic Storms, Artificial Neural Networks, Solar Winds, NARX, Forecasting, Interplanetary Magnetic Field
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.5 No.2,
May
16,
2014
ABSTRACT:
A nonlinear autoregressive approach with exogenous input is used as a
novel method for statistical forecasting of the disturbance storm time index, a
measure of space weather related to the ring current which surrounds the Earth,
and fluctuations in disturbance storm time field strength as a result of
incoming solar particles. This ring current produces a magnetic field which
opposes the planetary geomagnetic field. Given the occurrence of solar activity
hours or days before subsequent geomagnetic fluctuations and the potential
effects that geomagnetic storms have on terrestrial systems, it would be useful
to be able to predict geophysical parameters in advance using both historical
disturbance storm time indices and external input of solar winds and the
interplanetary magnetic field. By assessing various statistical techniques it
is determined that artificial neural networks may be ideal for the prediction
of disturbance storm time index values which may in turn be used to forecast geomagnetic
storms. Furthermore, it is found that a Bayesian regularization neural network
algorithm may be the most accurate model compared to both other forms of
artificial neural network used and the linear models employing regression
analyses.