## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c} \hline \multicolumn{13}{c}{LSTM} & \multicolumn{13}{c}{**ANN**} & \multicolumn{1}{c}{**SVM**} \\ \hline  & **R** & **NSE** & **KCE** & **PI** & **RSR** & **RMSE** & **R** & **NSE** & **KCE** & **PI** & **RSR** & **RMSE** & **R** & **NSE** & **KCE** & **PI** & **RSR** & **RMSE** \\ \hline M01 & 0.6087 & 0.2715 & 0.5612 & 0.1148 & 0.2289 & 0.8506 & 0.5161 & 0.2016 & 0.4652 & 0.1275 & 0.8905 & 3.3086 & 0.5451 & 0.2932 & 0.3992 & 0.1177 & 0.2255 & 0.8378 \\ M02 & 0.6461 & 0.3678 & 0.5519 & 0.1045 & 0.2133 & 0.7924 & 0.5568 & 0.2617 & 0.4510 & 0.1914 & 0.5864 & 3.1816 & 0.5818 & 0.3340 & 0.4431 & 0.1162 & 0.2189 & 0.8133 \\ M03 & 0.6511 & 0.3784 & 0.5466 & 0.1033 & 0.2135 & 0.7858 & 0.5717 & 0.2788 & **0.5195** & **0.1199** & **0.8964** & **3.1436** & 0.6120 & 0.3743 & 0.4613 & 0.1062 & 0.2122 & 0.7883 \\ M04 & 0.4971 & 0.1613 & 0.1391 & 0.1323 & 0.2487 & 0.9127 & 0.5663 & 0.2021 & 0.5544 & 0.1234 & 0.8902 & 3.3075 & **0.6160** & **0.3792** & **0.4663** & **0.1055** & **0.2114** & **0.7853** \\ M05 & **0.5651** & **0.3818** & **0.5910** & **0.1030** & **0.2109** & **0.7836** & 0.2128 & -1.9460 & 0.0209 & 0.3048 & 1.7106 & 0.3554 & 0.6167 & 0.3787 & 0.4678 & 0.1055 & 0.2114 & 0.7856 \\ \multicolumn{13}{c}{LSTM-W} & \multicolumn{13}{c}{**SVM-W**} \\ \hline  & **R** & **NSE** & **KCE** & **PI** & **RSR** & **RMSE** & **R** & **NSE** & **KCE** & **PI** & **RSR** & **RMSE** & **R** & **NSE** & **KCE** & **PI** & **RSR** & **RMSE** \\ \hline M01 & 0.6709 & 0.4055 & 0.5991 & 0.0998 & 0.2068 & 0.7684 & **0.5274** & **-0.0131** & **0.5238** & **0.1426** & **1.0031** & **3.2720** & 0.6754 & 0.4516 & 0.5858 & 0.0966 & 0.1997 & 0.7381 \\ M02 & 0.6457 & 0.3005 & 0.6260 & 0.1076 & 0.2195 & 0.8155 & 0.1137 & -0.5921 & 0.1041 & 0.2451 & 1.2575 & 4.6720 & 0.6775 & 0.4513 & 0.5976 & 0.0955 & 0.1997 & 0.7383 \\ M03 & 0.9490 & 0.8646 & 0.9282 & 0.0410 & 0.0989 & 0.3647 & 0.4148 & -1.5852 & 0.0588 & 0.2459 & 1.6024 & 5.9534 & 0.8453 Another forecasting method used for this study region is SARIMA, a stochastic model that also accounts for the seasonal effects. The findings from SARIMA are presented in Table 3. Besides, in this study, we created many models in the analysis with SARIMA. But we added the three most effective models, MR1, MR2, and MR3, these models estimated output value, which is the wind speed, in the best way, as performance metrics to the study. Among all SARIMA models, MR1 demonstrated the best performance metrics, achieving R = 0.7411, NSE = 0.5455, KGE = 0.6745, PI = 0.0856, RSR = 0.6735, and RMSE = 2.6348. When compared with machine learning algorithms performed without WT in terms of overall performance, MR1 ranked second, following LSTM-M05. Therefore, SARIMA (particularly MR1) is one of the preferred methods when WT is not applied in this region for wind speed forecasting. Figure 7b shows a comparison of this model with observation values. Three different SARIMA models (MR1, MR2, and MR3) were developed, each having distinct input structures, and their results are shown in Table 4 and Figure 8.  
The time series model was systematically examined through three stages: identification, parameter estimation, and diagnostic checking. To construct an appropriate ARIMA model, it is essential for the time series to be stationary. The ACF and PACF plots for the wind data, presented in Figure 8, indicate the orders of the AR and MA components, along with skewness and noticeable significant deviations. Therefore, in this study, seasonal differencing was applied to ensure stationarity. In this study, a stochastic SARIMA model was considered, and multiple SARIMA models were tested to determine the best-fitting model. Based on the AIC and BIC criteria, the optimal SARIMA model was identified as SARIMA (0,0,0)(8,1,0)\({}_{12}\). Table 4a presents some of the tested models along with their corresponding AIC and BIC values. The parameters of the selected best-fitting model are provided in Table 4b,\(c\).