###### Abstract  
Wind speed is a critical parameter for both energy applications and climate studies, particularly under changing climatic conditions and has attracted increasing research interest from the scientific community. This parameter is of interest to both researchers interested in climate change and researchers working on issues related to energy production. Based on this, in this study, prospective analyses were made with various machine learning algorithms, the long-short term memory (LSTM), the artificial neural network (ANN), and the support vector machine (SVM) algorithms, and one of the stochastic methods, the seasonal autoregressive integrated moving average (SARIMA), using the monthly wind data obtained from Bodo. In these analyses, five different models were created with the assistance of cross-correlation. The models obtained from the analyses were improved with the wavelet transformation (WT), and the results obtained were evaluated for the correlation coefficient (R), the Nash-Sutcliffe model efficiency (NSE), the Kling-Gupta efficiency (KGE), the performance index (PI), the root mean standard deviation ratio (RSR), and the root mean square error (RMSE). The results obtained from this study unveiled that LSTM emerged as the best performance metric in the M04 model among other models (R = 0.9532, NSE = 0.8938, KGE = 0.9463, PI = 0.0361, RSR = 0.0870, and RMSE = 0.3248). Another notable finding obtained from this study was that the best performance values in analyses without WT were obtained with SARIMA. The results of this study provide information on forward-looking modeling for institutions and decision-makers related to energy and climate change. wind speed; LSTM; ANN; SVM; wavelet transform; machine learning +
Footnote †: journal: Applied Sciences  
1 Research Development Institution Coordinator Office, Gazi University, Ankara 06560, Turkiye; [EMAIL_ADDRESS]  
2 Faculty of Engineering and Natural Sciences, Civil Engineering, Ankara Yildirum Beyazt University, Ankara 06010, Turkey
systems, with far-reaching consequences for Arctic communities and industries dependent on these resources [5; 6; 7]. Accurate wind resource assessment and advanced forecasting techniques are critical for addressing these challenges and maximizing the potential of wind as a sustainable energy resource. Numerous methods have been applied to forecast wind separately or by a combination of stochastic methods, and machine learning. One prominent approach is the integration of machine learning with traditional statistical methods. For instance, [PERSON] and [PERSON] [8] propose a hybrid framework that combines long short-term memory (LSTM) networks with an unscented Kalman filter (UKF) to enhance wind nowcasting accuracy in aviation applications. This combination allows for better handling of the uncertainties inherent in wind data, demonstrating the effectiveness of hybrid models in forecasting tasks. Similarly, [PERSON] and [PERSON] [9] present a hybrid model that utilizes empirical mode decomposition (EMD) and multiple kernel Learning for short-term wind speed forecasting, showcasing the potential of combining different methodologies to improve prediction performance [9]. [PERSON] et al. [10] also presented a hybrid machine learning technique composed of wavelet transform (WT) and fuzzy ARTMAP (FA) network for short-term wind speed forecasting. The application of various machine learning algorithms has also been extensively documented. For example, [PERSON] et al. [11] analyze wind speed data with multiple ML techniques, including SVM, the multilayer feed-forward neural networks, and the adaptive neuro-fuzzy inference system (ANFIS), finding SVM to be the most effective. This aligns with findings from other studies, such as those by [PERSON] and [PERSON] [12], who categorize forecasting methods into physical, statistical, and machine learning-based approaches, emphasizing the growing importance of ML in wind power forecasting. Furthermore, [PERSON] et al.