Note that since we don’t have an observed value for ε 106, we use the theoretical mean value, namely zero. ![]() Thus, the forecast value at time i = 106 is ![]() The predicted (or forecasted) value at time 106 (cell Y113) is based on the equation that defines the ARIMA(1,1) process, namely The predicted values in Y for the observed data in the time series (range Y8:Y112) is simply the data element minus the residual e.g. The result is shown in Figure 1, where we have omitted the data for times 5 through 102 to save space.Ĭolumns V, W and X are just copies of columns E, F and G from Figure 1 of Calculating ARMA Coefficients using Solver. We now show how to create forecasts for a time series modelled by an ARMA( p,q) process.Įxample 1: Create a forecast for times 106 through 110 based on the ARMA(1,1) model created in Example 1 of Calculating ARMA Coefficients using Solver.
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