The CFA results showed satisfactory fit indices at each wave: chi square ([chi square])/degrees of freedom (df) ratio = 2.27, standardized root mean square
residual (SRMR) = .064, root mean square
error of approximation (RMSEA) = .077, goodness-of-fit index (GFI) = .90, comparative fit index (CFI) = .95.
Advantages of the mean absolute error (MAE) over the root mean square
error (RMSE) in assessing average model performance.
In this study, the mean square error (MSE), root mean square
error (RMSE), normalized root mean square
error (NRMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were calculated using the following equations, which are the indexes of forecasting accuracy:
Within the scope of time domain parameters, SDNN (standard deviation of NN Intervals), SDANN (standard deviation of NN in All 5-Minute Segments), SDNN index (mean of five minute standard deviation of NN intervals), RMSSD (Root Mean Square
of Successive NN Interval Differences) and PNN50 (proportion of NN50) were evaluated.
Based on this, Chi-square ([chi square])/degrees of freedom (df) were calculated to be 2.807, standardized root mean square
residual (SRMR) to be 0.093, adjusted goodness of fit index (AGFI) to be 0.854, comparative fit index (CFI) to be 0.879, and root mean square
error of approximation (RMSEA) to be 0.078 (Table 2).
Modeling the break eliminates forecast bias, reduces root mean square
forecast error, and significantly increases the signaling power of the PMI.
In each band, the high protruding frequency is extracted as the feature, and root mean square
(RMS) value for these protruding frequencies are used for fault detection.
The Root Mean Square
Residual (RMR) index between 0 and 1 and the root mean squared error approximation (RMSEA) index lower than 0.05 are considered to be an indicator of good fit (28-30).
RMSE: Root Mean Square
Error; NRMSE: Normalized Root Mean Square
Error; BIAS: BIAS index; r: correlation coefficient.
It was performed as follows: (1) 90% of the calibration dataset samples were used to form the calibration model, and the remaining 10% samples were used to validate this model, and the procedure was repeated by 10 times; (2) the root mean square
error of cross-validation (RMSECV) was then calculated as follows:
Caption: Figure 2: Root mean square
error for the AOA in degrees vs.
We chose to use root mean square
residual (RMR), Standardized Root Mean Square
Residual (SRMR) and Root Mean Square
Error of Approximation (RMSEA) for a number of reasons.