## 6 Discussion
### Sample Selection  
Currently, there is no explicit theory about how to select the number of training and testing sample. Too small and the input sample cannot train the model well, while too large and the sample will cause over-fitting. In terms of wind speed forecasting, how to select the number of input set is still a difficult and challenging issue [35; 36]. It can only get the optimal input set by experiments. Besides, we organized and list the simulation results based on different ratio between training and testing samples in Table 7. Taking the one-step forecasting result of dataset one as an example, the MAPE values of the proposed model are 5.40%, 4.92%, 4.79%, 4.95%, and 5.11% when the ratio of training and testing sample is set to 7.3, 8.2, 9:1, 14:1, and 29:1, respectively. The forecasting accuracy of other ratios are all lower than the ratio 9:1\({}^{\prime}\)s accuracy. Table 1 below shows that the experiment we took before choosing a suitable ratio of training sample and testing sample. It can be indicated that when the ratio of training and testing sample is set to 9:1, the performance of wind speed forecasting outperforms the other ratios used in the experiment. Thus, based on the experiments and experience sample data are selected.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**MAE**} & **MAPE (\%)** & **RMSE** & **WI** & **E\({}_{\text{NS}}\)** & **E\({}_{\text{LM}}\)** \\ \hline \multirow{3}{*}{**Dataset 1**} & 1-Step & 0.1815 & 4.79 & 0.3513 & 0.9838 & 0.9332 & 0.7600 \\  & 2-Step & 0.3622 & 6.19 & 0.4546 & 0.9720 & 0.8874 & 0.6699 \\  & 3-Step & 0.5895 & 10.14 & 0.7354 & 0.9249 & 0.7115 & 0.4609 \\ \hline \multirow{3}{*}{**Dataset 2**} & 1-Step & 0.1841 & 3.07 & 0.2342 & 0.9929 & 0.9713 & 0.8388 \\  & 2-Step & 0.3962 & 6.53 & 0.5034 & 0.9583 & 0.8355 & 0.6310 \\  & 3-Step & 0.6579 & 10.70 & 0.8291 & 0.8887 & 0.6073 & 0.3925 \\ \hline \multirow{3}{*}{**Dataset 3**} & 1-Step & 0.2120 & 2.69 & 0.2463 & 0.9918 & 0.9670 & 0.8268 \\  & 2-Step & 0.4814 & 5.97 & 05308 & 0.9529 & 0.8306 & 0.5820 \\  & 3-Step & 0.8694 & 9.52 & 0.8329 & 0.8599 & 0.5705 & 0.3240 \\ \hline \multirow{3}{*}{**Dataset 4**} & 1-Step & 0.3028 & 3.91 & 0.2679 & 0.9839 & 0.9350 & 0.7565 \\  & 2-Step & 0.4275 & 5.12 & 0.3652 & 0.9645 & 0.8604 & 0.6667 \\ \cline{1-1}  & 3-Step & 0.5310 & 8.03 & 0.5437 & 0.9189 & 0.6883 & 0.4797 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of multi-step forecasting of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{
\begin{tabular}{c} **Forecasting** \\ **Steps** \\ \end{tabular} } & \multicolumn{4}{c}{**Ratio Between the Training Sample and Testing Sample**} \\ \cline{3-6}  & & **7:3** & **8:2** & **9:1** & **14:1** & **29:1** \\ \hline \multirow{3}{*}{Dataset 1} & 1-step & 5.40 & 4.92 & **4.79** & 4.95 & 5.11 \\  & 2-step & 9.57 & 8.15 & **6.19** & 8.82 & 8.97 \\  & 3-step & 13.94 & 13.46 & **10.14** & 14.84 & 15.73 \\ \hline \multirow{3}{*}{Dataset 2} & 1-step & 4.16 & 3.91 & **3.07** & 3.54 & 3.76 \\  & 2-step & 9.32 & 7.52 & **6.53** & 7.32 & 8.44 \\  & 3-step & 12.69 & 11.24 & **10.70** & 11.15 & 11.80 \\ \hline \multirow{3}{*}{Dataset 3} & 1-step & 3.48 & 3.28 & **2.69** & 3.41 & 3.97 \\  & 2-step & 6.62 & 6.12 & **5.97** & 6.15 & 6.72 \\  & 3-step & 10.73 & 9.82 & **9.52** & 9.84 & 10.15 \\ \hline \multirow{3}{*}{Dataset 4} & 1-step & 4.88 & 4.09 & **3.91** & 4.16 & 4.51 \\  & 2-step & 7.26 & 6.65 & **5.12** & 5.52 & 5.84 \\ \cline{1-1}  & 3-step & 11.01 & 10.39 & **8.03** & 8.85 & 8.99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: MAPE (%) values of different ratios between training and testing sample of the proposed model.