## 3 Experimental Results and Discussion
### Practical Sample 2: Forecasting Inbound FDI in Myanmar  
After implementing an open-door policy, Myanmar has become an attractive destination for foreign direct investment (FDI) in recent years. The trend of the development of Myanmar's FDI is interesting in academic research and business analysis. However, due to the unstable political situation in this nation, foreign investment is a high-risk business. Currently, Myanmar's statistical data is not fully significant and has a fluctuating characteristic. For these reasons, forecasting the trends in Myanmar's FDI is difficult using traditional forecasting techniques. Thus, this study aims to challenge the predictive capability of the DGM(2,1) and its modified models, and the available data for Myanmar's inbound FDI between 2007 and 2013 has been used to conduct performance testing of the proposed models. Empirical results are shown in Table 3 and Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Actual Values \({}^{\star}\)**} & \multicolumn{5}{c}{**Relative Percentage Error (\%)**} \\ \cline{3-6}  & & **DGM(2,1)** & **MDGM(2,1)** & **FDGM(2,1)** & **FMDGM(2,1)** \\ \hline
2007 & 710 & 0 & 0 & 0 & 0 \\
2008 & 863 & 8.92 & 1.62 & 12.33 & 10.53 \\
2009 & 973 & 1.60 & 35.98 & 10.93 & 12.53 \\
2010 & 1285 & 9.56 & 18.89 & 8.28 & 7.07 \\
2011 & 2200 & 36.06 & 19.43 & 4.84 & 5.54 \\
2012 & 2246 & 24.27 & 8.08 & 4.74 & 4.05 \\
2013 & 2621 & 21.89 & 7.93 & 4.06 & 4.65 \\ \hline \multicolumn{2}{c}{_RMSE_} & 461.8 & 271.08 & 105.9 & 107.9 \\ \hline \multicolumn{2}{c}{_MAPE_ (\%)} & 17.05 & 15.32 & 7.53 & 7.4 \\ \hline Precision rate (\%) & 82.95 & 84.68 & 92.47 & 92.6 \\ \hline Forecasting grade & Unqualified & Unqualified & Good & Good \\ \hline \hline \end{tabular}  
* * Inbound foreign direct investment in Myanmar (USD million). Data taken from the website of the United Nations Conference on Trade and Development (UNCTAD).  
\end{table}
Table 3: Forecasting results for Myanmar’s inbound FDI.  
Figure 1: Comparison of the forecasting capability of different DGM(2,1) models for Thailand’s medical tourism revenue.  
From the chart in Figure 2, it seems that the data for Myanmar's inbound FDI occurs with rapid fluctuation and the predicted curve of the DGM(2,1) does not fit with the line of actual values. The _RMSE_ and _MAPE_ values are described in Table 3. According to this, the _RMSE_ values of the DGM(2,1), MDGM(2,1), FDGM(2,1), and FMDGM(2,1) models are 461.8, 271.08, 105.9 and 107.1, respectively. This means that the difference between predicted values and observed values of the DGM(2,1) is larger than that of MDGM(2,1), FDGM(2,1) and FMDGM(2,1). Additionally, the _MAPE_ index of DGM(2,1) is 17.05% while that of MDGM(2,1), FDGM(2,1) and FMDGM(2,1) are 15.32%, 7.53% and 7.40%, respectively. It is claimed that the _MAPE_ index of DGM(2,1) is the highest in four models. For this reason, it can be concluded that the DGM(2,1) has lower accuracy than the rest of the three modified models.  
As shown in Table 3, the precision rate of the DGM(2,1) and MDGM(2,1) models does not reach the qualified level, which is 82.95% and 84.68%, respectively. However, the FDGM(2,1) and FMDGM(2,1) obtained a good level of forecasting, which were 92.47% and 92.6%, respectively.