1. Nowferesti, M. (2016), Unit root and integrating in econometrics, Rasa Publication, Tehran, Iran.[In persian]

2. Akashi, F., Dette, H. and Liu,Y. (2018). Change point detection in autoregressive models with no moment assumptions. Journal of Time Series Analysis,763-786. [DOI:10.1111/jtsa.12405]

3. Chakar, S., Lebarbier, E., Levy-Leduc, C. and Robin, S. (2017). A robust approach for estimating change-points in the mean of an AR (1) process. Bernoulli, 23(2), 1408-1447. [DOI:10.3150/15-BEJ782]

4. Cryer, J. and Chan, K. S.(2008). Time Series Analysis with Applications in R. Springer, New York. [DOI:10.1007/978-0-387-75959-3]

5. Dokumentov, A., and Hyndman, R. J.(2022). STR: Seasonal-trend decomposition using regression. .Informs Journal on Data Science, 1(1), 50-62. [DOI:10.1287/ijds.2021.0004]

6. Gombay, E. (2008). Change detection in autoregressive time series. Journal of Multivariate Analysis, 99(3),451-464. [DOI:10.1016/j.jmva.2007.01.003]

7. Hrishnaiah, P. R., and Miao, B. Q. (1988).Review about estimation of change point. Handbook in Statistics. Quality Control and Reliability, North-Holland,375-402. [DOI:10.1016/S0169-7161(88)07021-X]

8. Huskova, M., Praskova, Z. and Steinebach, J. G. (2020) . Estimating a gradual parameter change in an AR (1)-process. Sląski Przegląd Statystyczny, 18 (24), 254-262. [DOI:10.15611/sps.2020.18.16]

9. Pang,T., Zhang,D. and Chong,T.L. (2014). Asymptotic inferences for an AR (1) model with a change point: stationary and nearly non-stationary cases. Journal of Time Series Analysis, 35 (2), 133-150. [DOI:10.1111/jtsa.12055]

10. Page, E.S. (1954). Continuous inspection schemes. Biometrika, 1, 100-115. https://doi.org/10.2307/2333009 [DOI:10.1093/biomet/41.1-2.100]

11. Priyadarshana, M. and Sofronov, G. (2012). A modified cross-entropy method for detecting change-points in the Sri-Lankan stock market. In Proceedings of the IASTED International Conference on Engineering and Applied Science, EAS 2012, 319-326. [DOI:10.2316/P.2012.785-041]

12. Sofronov, G., Polushina, T. and Priyadarshana, M. (2012).Sequential change-point detection via the rossentropy method. In B. Reljin and S. Stankovic (Eds.), The 11th Symposium on Neural Network Applications in Electrical Engineering , 185-188. [DOI:10.1109/NEUREL.2012.6420004]

13. Sofronov,G. and Ma, L. (2017). Change-point detection in time series data via the Cross-Entropy method. In Proceedings of the 22nd International Congress on Modelling and Simulation, Tasmania, Australia, 8 December 2017;pp. 195-201.

14. Shao, X. and Zhang, X. (2010). Testing for change points in time series. Journal of the American Statistical Association, 105(491), 1228-1240. [DOI:10.1198/jasa.2010.tm10103]

15. Timmer, D. H. and Pignatiello Jr, J. J. (2003). Changepoint estimates for the parameters of an AR(1) process. Quality and Reliability Engineering International, 19(4): 355-369. [DOI:10.1002/qre.589]

16. Xie, X., Brown, J.S., Bush, D., and Eckert, C.A. (2005). Bubble and dew point measurements of the ternary system carbon dioxide + methanol + hydrogen at 313.2 k. Journal of Chemical Engineering Data, 50( 3), 780-783. [DOI:10.1021/je0498614]