Neural network prediction of AE data

Details Neural network prediction of AE data Neural network prediction of AE data

Oct 1997

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1997georl..24.2403t&link_type=abstract

Geophysical Research Letters, Volume 24, Issue 19, p. 2403-2406

Astronomy and Astrophysics

Astrophysics

12

Magnetospheric Physics: Magnetospheric Configuration And Dynamics, Magnetospheric Physics: Solar Wind/Magnetosphere Interactions, Magnetospheric Physics: Storms And Substorms, Solar Physics, Astrophysics, And Astronomy: Magnetic Fields

Scientific paper

Neural network (NN) models were constructed to study prediction of the AE index. Both solar wind (vBz) and previous observed AE inputs were used to predict AE data for different numbers of time steps ahead. It seems that prediction of the original unsmoothed AE data is possible only for 10 time steps (25 min) ahead. The predicted time series of the AE data for 50 time steps (125 min) ahead was found to be dynamically different from the original time series. It is possible that the NN model cannot reproduce the turbulent part of the power spectrum of the AE data. However, when using smoothed AE data the prediction for 10 time steps ahead gave an NMSE of 0.0438, and a correlation coefficient of 0.98. The predictive ability of the model gradually decreased as the lead time of the predictions was increased, but was quite good up to predictions for 30 time steps (75 min) ahead.

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