A Nonlinear Autoregressive Approach to Statistical Prediction of Disturbance Storm Time Geomagnetic Fluctuations Using Solar Data

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.

Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses

“…Furthermore, the specific application of wavelet decomposition in combination with regression analysis was proven more effective than standard processing when compared to neural network models. That predictive space weather modelling has seen increased use and effectiveness recently [1] suggests a further need to examine the potential use of ANNs in this specific context. Along with the current results, this research implies the need to expand data prediction to alternate statistical techniques which may improve both accuracy and overall understanding of the phenomena in question [13].…”

“…Furthermore, these techniques should be explored for a range of space weather events for which early warning applications may be relevant. Predictive modelling of space weather phenomena could be beneficial to a diverse array of research areas including microelectronic functioning [9], solar-geomagnetic [1] and interplanetary conditions [16], and even human biology [17].…”

“…Although standard analyses may be perfectly capable of achieving specific research goals, a combination of analytical methods can often improve or enhance existing results. While neural networks have previously been used for prediction in a number of areas, including space weather [1], pattern recognition [11], and medical diagnosis [12], applying signal processing techniques from wavelet analysis appears capable of introducing greater accuracy for statistical modelling in this context [4,10].…”

“…Given minute variations in statistical results upon subsequent re-training of a particular network, large numbers of individual ANNs may be trained and tested in order to average the outputs for more accurate performance results. While autoregressive functions have previously been applied to neural network analyses for prediction of various space weather phenomena [1], exploration of advance statistical prediction has recently been examined without the necessity for strictly autoregressive network feedback. Furthermore, the combination of both ANN models and wavelet decomposition methods has proven effective in other areas of environmental prediction [4].…”

“…The use of advanced computational models, particularly artificial neural networks, in the long-term forecasting of various environmental factors has recently seen a surge in increased application [1]. Artificial neural networks (ANN) are nonlinear methods of artificial machine learning based on the nervous system.…”