[١]ﻃﺎﻫﺮی، م. (1375) ، آﺷﻨﺎﻳﻲ ﺑﺎ ﻧﻈﺮﻳﻪ ﻣﺠﻤﻮﻋﻪﻫﺎی ﻓﺎزی. اﻧﺘﺸﺎرات ﺟﻬﺎد داﻧﺸﮕﺎﻫﻲ داﻧﺸﮕﺎه ﻓﺮدوﺳﻲ ﻣﺸﻬﺪ.
[٢] ﻃﺎﻫﺮی، س.، اﮐﺒﺮی، م. ق. و ﺣﺴﺎﻣﯿﺎن، غ. ر. (1403)، ﻣﺪلﺳﺎزی ﻣﯿﺎﻧﮕﯿﻦ ﻣﺘﺤﺮک ﺑﺮ اﺳﺎس α -ﺷﮏ ﻣﺘﻐﯿﺮﻫﺎی
ﺗﺼﺎدفی ﻓﺎزی، ﻣﺠﻠﻪ ﻋﻠﻮم آﻣﺎری، ٨١ ، ٣٠١-٧٢١.
[٣] ﻣﺤﻤﺪی، ح.، اﮐﺒﺮی، م. ق. و ﺣﺴﺎﻣﯿﺎن، غ. ر. (1403)، ﻣﺪلﺳﺎزی اﺗﻮ رﮔﺮﺳﯿﻮن ﺑﺮ اﺳﺎس ﺗﺎﺑﻊ ﺗﮑﯿﻪﮔﺎه ﻣﺘﻐﯿﺮﻫﺎی
ﺗﺼﺎدفی ﻓﺎزی، ﻣﺠﻠﻪ ﻋﻠﻮم آﻣﺎری ،٨١، ٣٧١-٢٩١.
[4] Asadolahi, M., Akbari, M.G., Hesamian, G., and Arefi, M. (2021) A robust support vector regression with exact predictors and fuzzy responses. International Journal of Approximate Reasoning, 132, 206-225.
[5] Bazaraa, M. S., Jarvis, J. J. and Sherali, H. D. (2010) Linear Programming and Network Flows, 4th Edition. New York: Wiley.
[6] Cai, z. (2001) ”Weighted Nadaraya-Watson regression estimation”, Statistics and Probability Letters, 51,307-318.
[7] Chen, S. (1996) Forecasting enrollments based on fuzzy time series, Fuzzy Sets and Systems, 81, 311–319.
[8] Doroudyan, M. H. and Niaki, S. T. A. (2021) Pattern recognition in Financial surveillance with the ARMA-GARCH time series model using support vector machine , Expert Systems with Applications.
[9] Ethem, A. (2004) Introduction to Machine Learning. The MIT Press Cambridge, Massachusetts Lon- don.
[10] GascardJinlun, G.C. and Rafizadeh, Z. (2019) ”Rapid decline of arctic sea ice volume, causes and consequences”, The Cryosphere Discuss, 1-29.
[11] Gui, B., Wei, X., Shen, Q., Qi, J. and Guo, L. (2015) Financial time series forecasting using support vector machine, IEEE.
[12] Hesamian, G. and Akbari, M.G. (2018) A semi-parametric model for time series based on fuzzy data. Iranian Journal of Fuzzy Systems, 26, 2953-2666.
[13] Hesamian, G., Torkian, F. and Yarmohammadi, M. (2022) A fuzzy non-parametric time series model based on fuzzy data. Iranian Journal of Fuzzy Systems, 19, 61-72.
[14] Hojati, M., Bector, C.R. and Smimou, K. (2005) A simple method for computation of fuzzy linear regression. European Journal of Operational Research, 166, 172–184.
[15] Hong, D. (2005) A note on fuzzy time-series model. Fuzzy Sets and Systems, 155,309–316.
[16] Izenman, A. J. (2008) Modern Multivariate Statistical Techniques (Regression, Classification, and Manifold Learning), International Statistical Review.
[17] Ruan, J., Wang, X. and Shi, Y. (2013) Developing fast predictors for large-scale time series using fuzzy granular support vector machines . Applied Soft Computing Journal, 13, 3981-4000.
[18] Song, Q. and Chissom, B. S. (1993) Fuzzy time series and its models. Fuzzy Sets and Systems, 54, 269–277.
[19] Song, Q., Leland, R. P. and Chissom, B. S. (1995) A new fuzzy time-series model of fuzzy number observations. fuzzy sets and systems, 73, 341–348.
[20] Suykens, J. A. K., Gestel, J. V., Brabanter, J. D., Moor, B. D. and Vandewalle, J. (2002) Least squares support vector machines. World Scientific Pub. Co., Singapore, 29-55.
[21] Vapnik, V. (1995) The Nature of Statistical Learning Theory, Springer-Verlag . New York.
[22] Zadeh, L.A. (1956) ”Fuzzy sets”. Information and Control, 8, 338-353.
[23] Zarei, R., Akbari, M. G. and Chaci, J. (2020) Modeling autoregressive fuzzy time series data based on semi-parametric methods. Soft Computing, 24, 7295–7304.
