Root mean squared error (RMSE) and mean

absolute error (MAE) statistics reported in the first two rows of Table 4 are relative measures to compare forecasts of GDP.

We considered the estimated variance and covariance matrix in the G study and, therefore, conducted a further estimation analysis regarding the universal scores and corresponding variance components in the four dimensions, and then the GC, DI, and, as per Yang and Zhang (2003), the ratio of the sum of the variance of the measurement target and the relative error of the measurement (relative signal-to-noise ratio), and the ratio of the sum of the measurement target variance and the sum of the measurement

absolute error variance (absolute signal-to-noise ratio).

The maximum

absolute error is tabulated in the form of Tables 1-3 for the considered examples in support of the predicted theory.

The following measures of quality of the time series predictions were used to evaluate the results: mean

absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), directional accuracy (DAC), relative

absolute error (RAE), and root relative squared error (RRSE).

The

absolute error of porosity at 500 [degrees]C was small, only 4.8%, while at 400 [degrees]C it was the largest, 11.4%, which was caused by the effect of the oil shale sample size on the results.

However this overlapping appears rather rarely and the AS algorithm noticeably decreases the mean

absolute error (i.e.

Table 1 lists the mean and standard deviation (SD) of the

absolute error.

Figure 4 presents the fact that the relative magnitude of the cost function J/[J.sub.1] and the mean

absolute error (MAE) of observation points of the adjoint model using the characteristic finite difference (CFD) scheme decline more quickly.

The result of the predictive model was judged by the mean

absolute error percentage (MAPE), as shown in Figure 8.

The

absolute error of and [y.sub.mb](t) and [y.sub.b] is presented in Table 1 and Figure 1.

Obtained results of the developed individual and integrated classification models are compared using accuracy, true positive rate, precision, F-measure, kappa statistic, mean

absolute error, and root mean squared error.