Neural net forecasting for geomagnetic activity

Details Neural net forecasting for geomagnetic activity Neural net forecasting for geomagnetic activity

Dec 1993

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993georl..20.2707h&link_type=abstract

Geophysical Research Letters (ISSN 0094-8276), vol. 20, no. 23, p. 2707-2710

Physics

35

Earth Magnetosphere, Forecasting, Geomagnetism, Interplanetary Magnetic Fields, Neural Nets, Solar Wind, Mathematical Models, Nonlinear Filters, Solar Planetary Interactions

Scientific paper

We use neural nets to construct nonlinear models to forecast the AL index given solar wind and interplanetary magnetic field (IMF) data. We follow two approaches: (1) the state space reconstruction approach, which is a nonlinear generalization of autoregressive-moving average models (ARMA) and (2) the nonlinear filter approach, which reduces to a moving average model (MA) in the linear limit. The database used here is that of Bargatze et al. (1985).

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