[1] Rezaei, B., Javan, M, and Zainali, K. (2013). Investigating the trend of wind speed changes in northwest Iran, Journal of Natural Geography, (13), 27-36. [In Persian]
[2] Ebrahimi, S. and Ghafarzadeh, N. (2016). Short-term forecasting of wind speed using three types of combinations of neural networks based on division and combination, Journal of Renewable and New Energies, (1), 44-51. [In Persian]
[3] Mujiri, A. and Momeni, R. (2018). Modeling and forecasting wind speed in Zabul using statistical data (from 2008 to 2017), Dust conference in Southwest Asia. [In Persian]
[4] Malaabasi, B. and Golshani, L. (2019). Evaluation of Time Series Patterns for Wind Speed Volatilities in Anzali Meteorological Station, Iranian Journal of Official Statistics Studies, Vol. 31 (2), 461-476. [In Persian]
[5] Mohammadpour, M., Biorani, H. and Arabi Balaghi, R. (2021). Choosing the right statistical model for the wind speed of Tabriz and Urmia stations, Journal of Statistical Sciences, Vol. 15 (1), 219-232. [In Persian]
[6] Jan Nisari, F. and Islamian, S. (2022). Variable forecasting of wind speed in Zayandeh Rood basin using time series, New Research in Sustainable Water Engineering, Vol. 1 (1), 27-43. [In Persian]
[7] Tahamipour, M., Abedi, S., Karimi Baba Ahmadi, R. and Ebrahimizadeh, M. (2015). Investigating the effect of renewable energies on real economic growth per capita in Iran, Iranian Energy Economy Research Journal, 5th year, (91), 53-77. [In Persian]
[8] Ghafarpour, R. and Jam, A. R. (2015). The use of renewable energy sources in order to provide safe energy in sensitive centers, Iranian Energy Journal, Vol. 19 (3), 167-180. [In Persian]
[9] Akhbari, R. and Amadeh, H. (2017). Modeling and forecasting the state of air pollutants in the city of Tehran, using the self-regression model with long-term memory feature, Journal of Environmental Sciences and Technology, Vol. 20 (1), 41-57. [In Persian]
[10] Zakari, Z., Shakri, A. and Mohammadi, T. (2019). Choosing the right model in investigating the contagion of turbulence between financial markets among selected Islamic oil exporting countries, Quarterly Journal of Applied Economic Theories, 7th year, (3), 1-24. [In Persian]
[11] Kiqbadi, A. R. and Ahmadi, M. (2015). Comparing the effectiveness of GARCH and ARCH methods in predicting the value at risk for choosing the optimal portfolio, Journal of Financial Accounting and Auditing Research, 8th year, (63), 63-85. [In Persian]
[12] Ewing, B. T., Kruse, J. B, and Schroeder, J. L. (2006). Time series analysis of wind speed with time varying turbulence. Environ metrics, Vol. 17 (2), 119-127.
[13] Ewing, B. T, Kruse, J. B, and Thompson, M. A. (2008). Analysis of time-varying turbulence in geographically-dispersed wind energy markets. Energy Sources, Part B, Vol. 3 (4), 340-347.
[14] Payne, J. E. and Carroll, B. (2009). Modeling wind speed and time-varying turbulence in geographically dispersed wind energy markets in China. Energy Sources. Part A, Vol. 31 (19), 1759-1769.
[15] Li, H., Li, R. and Zhao, (2011). Wind speed forecasting based on autoregressive moving average- exponential generalized autoregressive conditional heteroscedasticity-generalized error distribution (ARMA-EGARCH-GED) model. International Journal of the Physical Sciences Vol. 6 (30), 6867 – 6871.
[16] Chen, H., Zhang, J., Tao, Y. and Tan, F. (2019). Asymmetric GARCH type models for asymmetric volatility characteristics analysis and wind power forecasting. Protection and Control of Modern Power Systems, 4-29
[17] Gupta, A., Kumar, A. and Boopathi, K. (2021). Day-ahead and intra-day wind power forecasting based on feedback error correction, IET Renewable Power Generation, Vol. 15 (1), 2840-2848.
[18] Engle, R. F. (1987). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica, Vol. 55 (2), 391-407.
[19] Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of econometrics, Vol. 31 (3), 307-327.
[20] Glosten, L. R, Jagannathan, R., and Runkle, D.E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The journal of finance, Vol. 48 (5), 1779-1801.
[21] Souri, A. (2016). Econometrics with Stata & Eviews Application. Tehran: Culture Press. [in Persian]
[22] Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach Econometric. Journal of the Econometric Society, 347-370.
[23] Engle, R. F. and Ng, V. K. (1993). Measuring and testing the impact of news on volatility. The journal of finance, Vol. 48 (5), 1749-1778.
[24] Chakrabarti, A. and Ghosh, J.K. (2011). AIC, BIC and Recent Advances in Model Selection. In Handbook of the Philosophy of Science, Vol. 7, 583-605.