## 3 Results and Discussion
### Forecast of Temperature
#### 3.1.3 With Input Data from Temperature and Wind Speed  
The optimal size of the sliding window is 1, and best results are shown in Table 5 and Figure 7. The data of wind speed in kilometers per hour (km/h) are used in this test. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**MSE in Temperature Forecasting**} \\ \hline \multirow{2}{*}{**Method**} & **MSE Average (2003–2007)** & **MSE Average (2003–2007)** \\  & **in South Tenerife** & **in Gran Canaria** \\ \hline \multirow{2}{*}{**SVM Linear**} & 0.140 & 0.100 \\  & **SVM RBF** & 0.239 & 0.166 \\  & **SVM Polynomial** & & \\ \(\gamma=1.42\times 10^{-4}\); \(d=2\); \(r=4\) & 0.150 & 0.110 \\  & **Regression tree** & 0.324 & 0.198 \\  & **Fit Linear Model** & 0.253 & 0.158 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Forecasting of temperature (\({}^{\circ}\)C), using temperature and precipitation (mm) as input data. Figure 6: Temperature forecasting in GC for the year 2004, using SVM linear (input: temperature and precipitation). 1.4. With Input Data from Temperature in \({}^{\circ}\)C and Temperature in Tenths of \({}^{\circ}\)C  
Table 6 and Figure 8 shows best results obtained. The optimal size of the sliding window is \(1\). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**MSE in Temperature Forecasting**} \\ \hline \multirow{2}{*}{**Method**} & **MSE Average (2003–2007)** & **MSE Average (2003–2007)** \\  & **in South Tenerife** & **in Gran Canaria** \\ \hline
**SVM Linear** & 0.136 & 0.073 \\
**SVM RBF** & 0.211 & 0.133 \\
**SVM Polynomial** & & \\
**SVM RBF** & 0.153 & 0.086 \\ \(\gamma=2.12\times 10^{-4}\) & & \\
**SVM Polynomial** & & \\
**\(\gamma=2.05\times 10^{-4}\); \(d=4\); \(r=5\) & & \\
**Regression tree** & 0.140 & 0.093 \\
**Regression tree** & 0.619 & 0.506 \\
**Fit Linear Model** & 0.261 & 0.163 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Forecasting of temperature (\({}^{\circ}\)C), using temperature and wind speed (km/h) as input data. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**MSE in Temperature Forecasting**} \\ \hline \multirow{2}{*}{**Method**} & **MSE Average (2003–2007)** & **MSE Average (2003–2007)** \\  & **in South Tenerife** & **in Gran Canaria** \\ \hline
**SVM Linear** & 0.139 & 0.101 \\
**SVM RBF** & 0.260 & 0.179 \\ \(\gamma=2.05\times 10^{-4}\); \(d=4\); \(r=5\) & & \\
**Regression tree** & 0.619 & 0.506 \\
**Fit Linear Model** & 0.261 & 0.163 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Forecasting of temperature (\({}^{\circ}\)C), using temperature and wind speed (km/h) as input data. Figure 7: Temperature forecasting in GC for the year 2004, using SVM polynomial (input: temperature and wind). 1.5. With Input Data from Temperature in \({}^{\circ}\)C and Temperature in Tenths of \({}^{\circ}\)C and Wind Speed  
The optimal size of the sliding window is 1, reaching best results showed in Table 7 and Figure 9. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**MSE in Temperature Forecasting**} \\ \hline \multirow{2}{*}{**Method**} & **MSE Average (2003–2007)** & **MSE Average (2003–2007)** \\  & **in South Tenerife** & **in Gran Canaria** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} **SVM Linear** \\ **SVM RBF** \\ \(\gamma=3.8\times 10^{-6}\) \\ **SVM Polynomial** \\ \(\gamma=0.75\times 10^{-5}\); \(d=2\); \(r=6\) \\ **Regression tree** \\ **Fit Linear Model** \\ \end{tabular} } & 0.158 & 0.159 \\  & 0.208 & 0.134 \\ \multirow{2}{*}{
\begin{tabular}{c} **SVM Linear RBF** \\ \(\gamma=3.8\times 10^{-6}\) \\ **SVM Polynomial** \\ \(\gamma=0.75\times 10^{-5}\); \(d=2\); \(r=6\) \\ **Regression tree** \\ **Fit Linear Model** \\ \end{tabular} & 0.150 & 0.087 \\  & 0.619 & 0.506 \\  & 0.261 & 0.163 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Forecasting of temperature (\({}^{\circ}\)C), using temperature (\({}^{\circ}\)C), temperature (tenths \({}^{\circ}\)C) and wind speed (km/h) as input data.