التنبؤ بسرعة الرياح الشهرية في منطقة سبها جنوب غرب ليبيا للفترة 2023-1972 باستخدام النموذج الموسمي الهجين SARIMA-GARCH

[1] شرميط، نهلة محمد. (2010). استخدام التحليل الإحصائي لبيانات المناخ الدالية، دراسة تطبيقية [رسالة ماجستير غير منشورة]. الأكاديمية الليبية، طرابلس، ليبيا.

[2] Intergovernmental Panel on Climate Change [IPCC]. (2022). Climate change 2022: Impacts, adaptation and vulnerability. Cambridge University Press. https://doi.org/10.1017/9781009325844

[3] Milad, S., Milićević, S., & Katić, V. A. (2023). Comparative Analysis of Wind Speed Distribution Models: A Case Study of Libya. In 2023 10th International Conference on Electrical, Electronic and Computing Engineering (IcETRAN) (pp. 1-6). IEEE. https://doi.org/10.1109/IcETRAN59631.2023.10392177

[4] Alharbi, F. R., & Csala, D. (2021). Wind speed forecasting using statistical and machine learning methods: A case study in Saudi Arabia. Energy Reports,7, 452–462. https://doi.org/10.1016/j.egyr.2021.01.007

[5] Rachid, M., Said, D., & Boumédiène, B. (2012). Wind Characteristics Analysis for Selected Site in Algeria. International Journal of Computer Applications, 56, 39-46.

[6] Bodlal, H. M. (2016). Analysis of solar and wind energy potential in Libyan desert. Journal of Arid Environments, 124, 15-22. https://doi.org/10.1016/j.jaridenv.2015.07.012

[7] Elmabruk, A. M., Aleej, F. A., & Badii, M. M. (2014). Estimation of wind energy in Libya. In 2014 5th International Renewable Energy Congress (IREC) (pp. 1-6). IEEE. https://doi.org/10.1109/IREC.2014.6826947

[8] El-Osta, A., & Khalifa, J. (1997). Wind energy potential in Libya: A survey. Renewable Energy, 11(4), 435-449. https://doi.org/10.1016/S0960-1481(97)00020-X

[9] Bunnag, T. (2024). Forecasting PM10 caused by Bangkok's leading greenhouse gas emission using the SARIMA and SARIMA-GARCH model. International Journal of Energy Economics and Policy, 14(1), 418-426. https://doi.org/10.32479/ijeep.14845

[10] Sharma, S. K., & Ghosh, S. (2016). Short-term wind speed forecasting: Application of linear and non-linear series models. International Journal of Green Energy, 13(14), 1490–1500. https://doi.org/10.1080/15435075.2016.1212200

[11] Poon, S. H., & Granger, C. W. J. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539. https://doi.org/10.1257/002205103765762743

[12] Bera, A. K., & Higgins, M. L. (1993). ARCH Models: Properties, Estimation and Testing. Journal of Economic Surveys, 7(4), 305–366. https://doi.org/10.1111/j.1467-6419.1993.tb00170.x

[13] Ewing, B.T., Kruse, J.B. and Schroeder, J.L. (2006), Time series analysis of wind speed with time-varying turbulence. Environmetrics, 17, 119-127. https://doi.org/10.1002/env.754

[14] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Economic Surveys,31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1

[15] Francq, C., & Zakoïan, J. M. (2019). GARCH models: Structure, statistical inference and financial applications (2nd ed.). Wiley. https://doi.org/10.1002/9781119540348

[16] Modarres, R., & Ouarda, T. B. (2013). Generalized autoregressive conditional heteroscedasticity modelling of hydrologic time series. Hydrological Processes, 27(22), 3174-3191. https://doi.org/10.1002/hyp.9415

[17] Chang, X., Gao, M., Wang, Y., & Hou, X. (2012). Seasonal autoregressive integrated moving average model for precipitation time series. Journal of Mathematics & Statistics, 8(4), 500-505. https://doi.org/10.3844/jmssp.2012.500.505

[18] Hung, W., & Hongxing, W. (1980). ARIMA Seasonal of Time Series and its Application to Long Range Weather Forecasting. In WMO Symposium on Probabilistic and Statistical Methods in Weather Forecasting.

[19] Tyass, I., Bellat, A., Raihani, A., Ansouri, K., & Khalili, T. (2022). Wind Speed Prediction Based on Seasonal ARIMA model. E3S Web of Conferences, 336, 00034. https://doi.org/10.1051/e3sconf/202233600034

[20] Saker, G. M. (2021). Evaluating ARIMA models for meteorological data in North Africa. Arabian Journal of Geosciences, 14(8), 600-615. https://doi.org/10.1007/s12517-021-07003-w

[21] Cryer, J. D., & Chan, K. S. (2008). Time series analysis: With applications in R (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-75959-3

[22] Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control (5th ed.). John Wiley & Sons. https://doi.org/10.1002/9781118619193

[23] Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987-1007. https://doi.org/10.2307/1912773

[24] Ghalanos, A. (2022). rugarch: Univariate GARCH models (R package version 1.4.9) [Computer software]. CRAN. https://cran.r-project.org/package=rugarch

[25] Tsay, R. S. (2010). Analysis of financial time series (3rd ed.). Wiley. https://doi.org/10.1002/9780470644560

[26] Hussin, N. H., Yusof, F., & Norrulashikin, S. M. (2021). Forecasting Wind Speed in Peninsular Malaysia: An Application of ARIMA and ARIMA-GARCH Models. Pertanika Journal of Science & Technology, 29(1), 343-362. https://doi.org/10.47836/pjst.29.1.19

[27] Liu, H., Tian, H. Q., & Li, Y. F. (2016). Comparison of two new ARIMA-ARCH and ARIMA-GARCH type copula approach for agricultural price forecasting. Statistics and Applications, 23(1), 197-216.

[28] Liu, H., Erdem, E., & Shi, J. (2011). Comprehensive evaluation of ARMA-GARCH(-M) approaches for modeling the mean and volatility of wind speed. Applied Energy, 88(3), 724-732. https://doi.org/10.1016/j.apenergy.2010.09.028

[29] Ling, S., & McAleer, M. (2003). Asymptotic theory for a vector ARMA-GARCH model. Econometric Theory, 19(2), 280-310. https://doi.org/10.1017/S0266466603192092

[30] Hussin, N. H., Yusof, F., & Norrulashikin, S. M. (2023). A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting. AIP Conference Proceedings, 2500(1), 020009. https://doi.org/10.1063/5.0116189

[31] Pandey, P. K., Tripura, H., & Pandey, V. (2019). Improving prediction accuracy of rainfall time series by hybrid SARIMA-GARCH modeling. Natural Resources Research, 28(3), 1125-1138. https://doi.org/10.1007/s11053-018-9436-0

[32] Abdelkarim, A. S., Danardono, D., Himawanto, D. A., & Atia, H. M. (2014). Analysis of wind speed data in East of Libya. International Journal of Engineering Research & Technology (IJERT), 3(12), 800-805.

]33[ محمد امراجع محمد وعلي خير صابر (2018). استخدام النموذج الموسمي المضاعف للتنبؤ بدرجات الحرارة الشهرية: دراسة حالة: مدينة طرابلس بشمال غرب ليبيا. مجلة جامعة سبها للعلوم البحثية والتطبيقية، 17(2)، 83-93.

[34] Almabrouk, A. R. (2019). Wind speed distribution in Fezzan region. Libyan Journal of Science, 29, 112-125.

[35] AccuWeather. (n.d.). Satellite weather map of Sabha, Libya. Retrieved January 15, 2026, from https://www.accuweather.com/ar/ly/sabha/230181/satellite/230181

[36] R Core Team. (2023). R: A language and environment for statistical computing (Version 4.3.1) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

[37] Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3), 1-22. https://doi.org/10.18637/jss.v027.i03

[38] Boshnakov, G. N., & Halliday, J. (2025). Sarima: Simulation and Prediction with Seasonal ARIMA Models (Version 0.9.5) [R package]. CRAN. https://cran.r-project.org/package=sarima

[39] Vandaele, W. (1983). Applied Time Series and Box-Jenkins Models. John Wiley & Sons.

[40] Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705

[41] Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346. https://doi.org/10.1093/biomet/75.2.335

[42] Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178. https://doi.org/10.1016/0304-4076(92)90104-Y

[43] Brockwell, P. J., & Davis, R. A. (1991). Time Series: Theory and Methods (2nd ed.). Springer. https://doi.org/10.1007/978-1-4419-0320-4

[44] Schwert, G. W. (1989). Tests for unit roots: A Monte Carlo investigation. Journal of Business & Economic Statistics, 7(2), 147-159. https://doi.org/10.1080/07350015.1989.10509723

[45] Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607. https://doi.org/10.1093/biomet/71.3.599

[46] Dritsaki, C. (2018). The Performance of Hybrid ARIMA-GARCH Modeling and Forecasting Oil Price. International Journal of Energy Economics and Policy, 8(3), 14-21.

[47] Qasim, T. B., Ali, H., Malik, N., & Liaquat, M. (2021). Forecasting Inflation Applying ARIMA Model with GARCH Innovation: The Case of Pakistan. Journal of Accounting and Finance in Emerging Economies, 7(2), 313-324. https://doi.org/10.26710/jafee.v7i2.1703

[48] Méndez-Gordillo, A. R., Campos-Amezcua, R., & Cadenas, E. (2022). Wind speed forecasting using a hybrid model considering the turbulence of the airflow. Renewable Energy, 196(c),422-431. https://doi.org/10.1016/j.renene.2022.06.147

[49] Taoussi, B., Boudia, S. M., & Mazouni, F. S. (2025). Wind speed forecasting using univariate and multivariate time series models. Stochastic Environmental Research and Risk Assessment, 39(2), 547-579. https://doi.org/10.1007/s00477-024-02881-2

[50] Manjunatha, B., Paul, R. K., Ramasubramanian, V., Kumar, A., Paul, M. Y., Ray, M., ... & Deb, C. K. (2025). A hybrid ARIMA-GARCH type copula approach for agricultural price forecasting. Statistics and Applications, 23(1), 197-216.

[51] Shreif, H. ., El-Osta, W., & Yagub, A. (2019). Wind Resource Assessment for southern part of Libya: Case Study of Hun. Solar Energy and Sustainable Development Journal, 8(1), 12–33. https://doi.org/10.51646/jsesd.v8i1.18

[52] Alowaa, M. I., Hossin, M. A., Said, H. A. H., Othman, O. A., & Wadan, M. I. (2025). Comparative Assessment of Wind Energy Resources Using NASA data and meteorological station Measurements, A Case Study of Sebha city, Libya. Journal of Emerging Technologies and Innovative Research. 12(12), 408-430.