## 3 The Model Processing and Analysis Forecasting Result
### Forecasting Results of Individual and Combined Models  
In this section, forecasting results of both individual and combined methods are presented. The real values and forecasting values for the four different income groups from 2013 to 2016, generated by all seven forecasting models, are listed in Appendix B. From Table 5, it can be concluded that the absolute values of the differences between the real values and the forecasting values, by means of the combined forecasting model, are no greater than four. Moreover, one-third of the twelve forecasting values derived from the proposed combination forecasting model are exactly equal to their real values. Thus, related analysis sufficiently reflects the superiority of the proposed combination forecasting model based on artificial intelligence optimization. Figure 2 presents the stack bars of forecast errors, including _MAPE_ and _RMSE_, of the seven forecasting models for the four income groups. Note that, in Figure 2, the _MAPE_ value is represented as a percentage. From Figure 2 and Table 5 we can see that the combined forecasting model can further improve forecast accuracy compared with individual regression models as evidenced by it always achieving the lowest forecast error. Based on the fitting results of six polynomial regression models from 2000 to 2012, the combined weight of each model is calculated according to the combined model theory. In order to get the optimal combined weight, cuckoo algorithm is used to optimize the combination weight and the forecasting results (2013-2016) of CS-combined model is calculated by the optimal combination weight. The CS-combined model was established for the tuberculosis prevalence rate in high income population, which fitted the trend of the original tuberculosis prevalence rate. The forecasting accuracy of CS-combined model is higher than the other model and could be used for the forecasting tuberculosis epidemic trend in high income group. The forecasting results show that the incidence of tuberculosis in the high income group has been declining year by year since 2013 and the decline in 2013-2016 fluctuated between 2% and 4%. The tuberculosis prevalence rate in upper-middle income group in 2013-2016 showed a decreasing trend. For the forecasting results of upper-middle income group from 2013 to 2016, the RMSE and MAPE of CS-combined forecasting model were 0.6307 and 0.4883% respectively, which indicated that the CS-combined model has better forecasting performance and can meet higher forecasting requirements. From another point of view, the CS-combined model can be used for other diseases forecasting. For the lower-middle income group, the RMSE and MAPE of CS-combined model are 0.2113% and 0.2270%, respectively. The forecasting result of CS-combined model indicates that the tuberculosis prevalence rate from 2013 to 2016 is also declining. The forecasting results of tuberculosis prevalence rate for low income group from 2013 to 2016 showed that RMSE and MAPE were 0.3556% and 0.1028%, respectively, and the forecasting values were close to the actual values, which indicate that the CS-combined model has good forecasting performance and application in the tuberculosis prevalence rate forecasting. The forecasting results of the combined model could be used for the prevention and control of tuberculosis in low income group, and provide reference for formulating measures. The above analysis shows that global tuberculosis control strategies and measures have obtained significant achievements, which effectively curb the trend of tuberculosis prevalence rate. **Remark:** The CS-combined model proposed in this paper can improve the forecasting accuracy, which combines the advantages of a variety of models and overcomes the influence of the characteristics of the tuberculosis prevalence rate time series on the forecasting results, such as fluctuating trend, small sample, randomness, and non-linearity.