Accuracy of the testing data results are expressed in terms of

mean absolute percentage error (MAPE).

WMAPE is relevant when there is a wide range of local area population sizes and preferable in such cases to the

Mean Absolute Percentage Error (MAPE), which effectively weights all observations equally:

t] is the sequence of random shocks Figure 3: Different models can be compared using criteria such as the Akaike's Information Criterion (AIC) or Schwarz's Bayesian Criterion (SBC) and Rsquare, where larger values indicate a better fit and Mean Absolute Error (MAE) and

Mean Absolute Percentage Error (MAPE) where smaller values indicate a better fit.

By contrast, the

mean absolute percentage error reflects the variability noted regardless of their direction and, as such, is the best and much more accurate predictor of differences from actual birth weight [11].

This study aims to analyse the statistical properties of the most widely used measure in forecast error estimation, the

mean absolute percentage error (MAPE), and to propose an alternative index, called Resistant MAPE or R-MAPE, which makes it possible to overcome the limitations detected in this measure.

The Mean Absolute Deviations (MAD) and the Revised

Mean Absolute Percentage Errors (RMAPE) for the length of k=25 in Tables1 by finding the average of absolute deviations and

mean absolute percentage errors for periods 8-17.

The forecasting performance of the two models was evaluated using the criteria of root mean square, mean absolute error, and

mean absolute percentage error.

Statistical techniques for analysing forecast errors include

mean absolute percentage error (MAPE) and root mean squared error (RMSE).

Estimated values were compared with measured values in terms of root mean square error, mean bias error, mean absolute bias error, mean percentage error, and

mean absolute percentage error.

The Root Mean Square Error (RMSE), Mean Absolute Error (MAE),

Mean Absolute Percentage Error (MAPE), Mean Absolute Deviation (MAD), Correlation (CORR), Data Assimilation (DA) and SIGN test are used here for forecasting accuracy measures.

The

mean absolute percentage error is the sum of the absolute deviation (regardless of their direction) reflecting the size of the overall predictive error in terms of actual birth-weight.

In such evaluations, it has been customary to use quantitative measures such as mean absolute error (MAE),

mean absolute percentage error (MAPE), or mean square error (MSE) to describe the characteristics of the forecasts.