## 3 Results  
This study employed both stochastic method, SARIMA, and machine learning algorithms, ANN, SVM, and LSTM to model the wind speed data measured for Bodo in Norway. The results obtained from these techniques were further enhanced using WT, significantly improving the predictive accuracy and capability.. Using WT enabled a more detailed analysis of the data and captured patterns that were not evident in the raw data. Consequently, the overall accuracy of the predictions improved significantly, enhancing the reliability of the findings. These improvements contribute to more reliable forecasts that provides valuable insights for decision-makers against natural disasters or develop energy planning strategies in the region. For this study, 70% of the monthly wind speed data from the study area was used for learning/training, while 30% was reserved for testing. The results from all techniques, both with and without WT, were shown in Table 3. Besides, in Table 3, the best results in each group are highlighted in bold. According to the results, the most successful model was M04 utilizing the LSTM algorithm with WT (LSTM-WM04). This model achieved the highest performance with R = 0.9532, NSE = 0.8938, KGE = 0.9463, PI = 0.0361, RSR = 0.0870, and RMSE = 0.3248, among all the results, including/incorporating input data t-I, t-2, t-11, and t-12. In contrast, without WT, the performance metrics for the same model were significantly lower (R = 0.4971, NSE = 0.1613, KGE = 0.1391, PI = 0.1323, RSR = 0.2457, and RMSE = 0.9127), demonstrating the improvements gained with WT. The M05 model with WT, also performed well, with metrics R = 0.9502, NSE = 0.8820, KGE = 0.9421, PI = 0.0381, RS = 0.0921, and RMSE = 0.3423. However, the input structure of these two models is different. Including a different input structure, t-10 lagged wind speed data, negatively impacted the performance of M05. Therefore, it can be concluded that the performance differences between these two models primarily depend on the selected input data. In the analysis performed without WT, it was observed that M05 outperformed the other models. Figure 7a shows a comparison of this model with observation values. Another method used in this study for wind speed prediction is ANN. The analyses revealed that there was a decrease in performance metrics across all ANN models when WT was applied. Therefore, ANN with WT is not recommended to predict wind speed in this region. In the analysis performed without WT, however, the most successful ANN model was M03, yielding performance metrics of R = 0.5717, NSE = 0.2788, KGE = 0.5195, PI = 0.1169, RSR = 0.8464, and RMSE = 3.1446. SVM is another machine learning algorithm used in this study. While the most successful SVM model in the analysis without WT was M04 (SVM-M04), whereas the most successful SVM algorithm in the analysis with WT was M05 (SVM-WM05). Although SVM-WM05 outperformed other SVM models, it still falls behind models LSTM-WM04, LSTM-WM05, and LSTM-WM03 in terms of performance. Overall, the application of WT improved the performance of all SVM models. However, compared to LSTM-based models, SVM models showed relatively lower performance.