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The study begins with a synthetic presentation of geological and geophysical data at this time. The geological data come from the detailed geological mapping superimposed over the previously elaborated geological maps (at scales 1: 200000 and 1: 50000). The data on the deep structure come from the magnetotelluric and seismic sections interpreted. Of course, the documentation obtained from previous referenced publications and the specialized sites brought an extra knowledge. We performed a multiparametric correlati...

The Auto-Regressive Integrated Moving Average (ARIMA) model is widely used to forecast non-stationary time series data. In a model of ARIMA (p, d, q), AR is autoregressive, p is the number of regression terms, MA is the moving average, q is the number of moving average terms, and d is the difference time to make the data a stationary series. It can be used to forecast the trend of geomagnetic disturbances. Firstly, the non-stationary historical data xt is processed by the d difference to develop the stable histori...