1. Chen, S. (1996), Forecasting Enrollments Based on Fuzzy Time Series, Fuzzy Sets and Systems, 81, 311-319. [DOI:10.1016/0165-0114(95)00220-0]

2. Cheng, C. H., Chen, T. L., Teoh, H.J., and Chiang, C. H. (2008), Fuzzy Time-Series Based on Adaptive Expectation Model for TAIEX Forecasting, Expert Systems with Applications, 34, 1126-1132. [DOI:10.1016/j.eswa.2006.12.021]

3. Cheng, S. H., Chen, S.M., and Jian W.S. (2016), Fuzzy Time Series Forecasting Based on Fuzzy Logical Relationships and Similarity Measures, Information Sciences, 327, 272-287. [DOI:10.1016/j.ins.2015.08.024]

4. Diamond, P. and Kloeden, P. (1994), Metric Spaces of Fuzzy Sets: Theory and Applications, World Scientific. [DOI:10.1142/2326]

5. D'Urso, P. and Gastaldi, T. (2002), An Orderwise Polynomial Regression Procedure for Fuzzy Data, Fuzzy Sets and Systems, 130, 1-19. [DOI:10.1016/S0165-0114(02)00055-6]

6. 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. [DOI:10.1109/TFUZZ.2018.2791931]

7. Hesamian, G., and Akbari, M. G. (2022), A Fuzzy Quantile Method for AR Time Series Model Based on Triangular Fuzzy Random Variables, Computational and Applied Mathematics, online. [DOI:10.1007/s40314-022-01826-1]

8. Hesamian, G. R., and Chachi, J. (2015), Two-Sample Kolmogorov-Smirnov Fuzzy Test for Fuzzy Random Variables, Statistical Papers, 56, 61-82. [DOI:10.1007/s00362-013-0566-2]

9. 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.

10. Hong, D. (2005), A Note on Fuzzy Time-Series Model, Fuzzy Sets and Systems, 155, 309-316. [DOI:10.1016/j.fss.2005.03.009]

11. Huarng, K. (2001), Effective Lengths of Intervals to Improve Forecasting in Fuzzy Time Series, Fuzzy Sets and Systems, 123, 387-394. [DOI:10.1016/S0165-0114(00)00057-9]

12. Jilani, T. A. and Burney, S. M. A. and Ardil, C. (2007), Multivariate High Order Fuzzy Time Series Forecasting for Car Road Accidents, International Journal of Computational Intelligence, 4, 15-20.

13. Kwakernaak, H. (1978), Fuzzy Random Variables-I. Definition and Theorem, Information Sciences, 15, 1-29. [DOI:10.1016/0020-0255(78)90019-1]

14. Näther. W. (2006), Regression with Fuzzy Random Data, Computational Statistics and Data Analysis, 51, 235-252. [DOI:10.1016/j.csda.2006.02.021]

15. Puri, M. L., and Ralescu, D. A. (1986), Fuzzy Random Variables, Journal of Mathematical Analysis and Applications, 114, 409-422. [DOI:10.1016/0022-247X(86)90093-4]

16. Taheri, S., Akbari, M. G., and Hesamian, G. (2024), Moving Average Modeling Based on α-value of Fuzzy Random Variables, Journal of Statistical Sciences, 18(1).

17. Rockafellar, R. T. (1970), Convex Analysis, Princeton University Press. [DOI:10.1515/9781400873173]

18. Ruey, C.T. (2005), Fuzzy Relation Analysis in Fuzzy Time Series Model, Computers and Mathematics with Applications, 49, 539-548. [DOI:10.1016/j.camwa.2004.07.014]

19. Song, Q., and Chissom, B. S. (1993), Fuzzy Time Series and Its Models, Fuzzy Sets and Systems, 54, 269-277. [DOI:10.1016/0165-0114(93)90372-O]

20. 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. [DOI:10.1016/0165-0114(94)00315-X]

21. Wu, H. C. (1999), The Central Limit Theorems for Fuzzy Random Variables, Information Sciences, 120, 239-256. [DOI:10.1016/S0020-0255(99)00063-8]

22. Zarei, R., Akbari, M. G., and Chachi, J. (2020), Modeling Autoregressive Fuzzy Time Series Data Based on Semi-Parametric Methods, Soft Computing, 24, 7295-7304. [DOI:10.1007/s00500-019-04349-w]