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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...