NEURAL-NET FORECASTING FOR GEOMAGNETIC-ACTIVITY

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].