1. . Kharin, Yu. Robustness in Statistical Forecasting / Yu. Kharin. – Heidelberg, New York, Dordrecht, London, 2013. – 356 p. https://doi.org/10.1007/978-3-319-00840-0

2. Weiss, C. An Introduction to Discrete-Valued Time Series / C. Weiss. – New York, 2018. – 480 p. https://doi.org/10.1002/9781119097013

3. Jacobs, P. A. Discrete time series generated by mixtures. I: correlation and runs properties / P. A. Jacobs, P. A. W. Lewis // Journal of the Royal Statistical Society. Series B: Statistical Methodology. – 1978. – Vol. 40, N 1. – P. 94–105. https://doi.org/10.1111/j.2517-6161.1978.tb01653.x

4. Raftery, A. E. A model for high-order Markov chains / A. E. Raftery // Journal of the Royal Statistical Society. Series B: Statistical Methodology. – 1985. – Vol. 47, N 3. – P. 528–539. https://doi.org/10.1111/j.2517-6161.1985.tb01383.x

5. Харин, Ю. С. Цепи Маркова с r-частичными связями и их статистическое оценивание / Ю. С. Харин // Доклады Национальной академии наук Беларуси. – 2004. – Т. 48, № 1. – С. 40–44.

6. Kharin, Yu. Markov chain of conditional order: properties and statistical analysis / Yu. Kharin, M. Maltsau // Austrian Journal of Statistics. – 2014. – Vol. 43, N 3. – P. 205–216. https://doi.org/10.17713/ajs.v43i3.32

7. Kharin, Yu. S. Statistical estimation of parameters for binary conditionally nonlinear autoregressive time series / Yu. S. Kharin, V. A. Voloshko, E. A. Medved // Mathematical Methods of Statistics. – 2018. – Vol. 27. – P. 103–118. https://doi.org/10.3103/s1066530718020023

8. Харин, Ю. С. Биномиальная условно нелинейная авторегрессионная модель дискретного временного ряда и ее вероятностно-статистические свойства / Ю. С. Харин, В. А. Волошко // Труды Института математики. – 2018. – Т. 26, № 1. – С. 95–105.

9. Харин, Ю. С. Нейросетевые модели биномиальных временных рядов в задачах анализа данных / Ю. С. Харин // Доклады Национальной академии наук Беларуси. – 2021. – Т. 65, № 6. – С. 654–660. https://doi.org/10.29235/1561-83232021-65-6-654-660

10. Kharin, Yu. S. Statistical analysis of parsimonious high-order multivariate finite Markov chains based on sufficient statistics / Yu. S. Kharin, V. A. Voloshko // Journal of Multivariate Analysis. – 2025. – Vol. 208. – Art. 105422. https://doi.org/10.1016/j.jmva.2025.105422

11. Vassiliou, P.-C. G. On the periodicity of non-homogeneous Markov chains and systems / P.-C. G. Vassiliou // Linear Algebra and its Applications. – 2015. – Vol. 471. – P. 654–684. https://doi.org/10.1016/j.laa.2015.01.017

12. Meligkotsidou, L. Forecasting with non-homogeneous hidden Markov models / L. Meligkotsidou, P. Dellaportas // Statistics and Computing. – 2011. – Vol. 21. – P. 439–449. https://doi.org/10.1007/s11222-010-9180-5

13. Dynkin, E. B. Markov Process / E. B. Dynkin. – Berlin, 1965. – 685 p. https://doi.org/10.1007/978-3-662-00031-1

14. Zucchini, W. Hidden Markov Models for Time Series: An Introduction Using R / W. Zucchini, I. L. MacDonald, R. Langrock. – CRC Press, 2016. – 398 p. https://doi.org/10.1201/b20790

15. Berchtold, A. High-Order Extensions of the Double Chain Markov Model / A. Berchtold; University of Washington Department of Statistics. – Seattle, 1999. – (Technical Report; N 356). – URL: https://stat.uw.edu/research/tech-reports/higher-order-extensions-double-chain-markov-model.