- [1] M. S. A. Abotaleb and T. Makarovskikh, Analysis of neural network and statistical models used for forecasting of disease infection cases, in International Conference on Information Technology and Nanotechnology (ITNT) , IEEE, (2021), 1–7.
- [2] M. Abotaleb and T. Makarovskikh, System for forecasting COVID-19 cases using time-series and neural networks models, Engineering proceedings, 5(1) (2021), 46.
- [3] M. Abotaleb, GLDMHO: Generalized Least Deviations Method High Order, GitHub repository, 2024, https://github.com/abotalebmostafa11/GLDMHO, Accessed: May 2024.
- [4] M. Abotaleb and P. Dutta, Optimizing long short-term memory networks for univariate time series forecasting: a comprehensive guide, Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence, Berlin, Boston: De Gruyter, (2024), 427–442.
- [5] M. Abotaleb and P. Dutta, Optimizing bidirectional long short-term memory networks for univariate time series forecasting: a comprehensive guide, Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence, Berlin, Boston: De Gruyter, (2024), 443–458.
- [6] M. Abotaleb and P. Dutta, Optimizing convolutional neural networks for univariate time series forecasting: a comprehensive guide, Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence, Berlin, Boston: De Gruyter, (2024), 459–472.
- [7] M. Abotaleb and P. Dutta, Optimizing gated recurrent unit networks for univariate time series forecasting: a comprehensive guide, Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence, Berlin, Boston: De Gruyter, (2024), 473–490.
- [8] M. Mijwil, M. Abotaleb, and P. Dutta, Artificial intelligence-based diagnosis and treatment of childhood bronchial allergies, Hybrid Information Systems: Non-Linear Optimization Strategies with Artificial Intelligence, Berlin, Boston: De Gruyter, (2024), 491–500.
- [9] P. H. Borghi, O. Zakordonets, and J. P. Teixeira, A COVID-19 time series forecasting model based on MLP ANN, Procedia Computer Science, Elsevier, 181 (2021), 940–947.
- [10] M. Kumar and M. Anand, An application of time series ARIMA forecasting model for predicting sugarcane production in India, Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, 9(1) (2014), 81–94.
- [11] A. L. S. Maia and F. A. T. De Carvalho, Holt’s exponential smoothing and neural network models for forecasting interval-valued time series, International Journal of Forecasting, Elsevier, 27(3) (2011), 740–759.
- [12] T. Makarovskikh and M. Abotaleb, Comparison between two systems for forecasting Covid-19 infected cases, Computer Science Protecting Human Society Against Epidemics: First IFIP TC 5 International Conference, ANTICOVID 2021, Virtual Event, June 28–29, 2021, Revised Selected Papers 1, Springer, (2021), 107–114.
- [13] T. Makarovskikh and M. Abotaleb, Hyper-parameter tuning for long short-term memory (LSTM) algorithm to forecast a disease spreading, VIII International Conference on Information Technology and Nanotechnology (ITNT), IEEE, (2022), 1–6.
- [14] T. Makarovskikh, A. Panyukov, and M. Abotaleb, Using General Least Deviations Method for Forecasting of Crops Yields, International Conference on Mathematical Optimization Theory and Operations Research, Springer, (2023), 376–390.
- [15] I. Naim, T. Mahara, and A. R. Idrisi, Effective short-term forecasting for daily time series with complex seasonal patterns, Procedia Computer Science, Elsevier, 132 (2018), 1832–1841.
- [16] A. B. S. Neto, T. A. E. Ferreira, M. C. M. Batista, and P. R. A. Firmino, Studying the performance of cognitive models in time series forecasting, Revista de Inform´atica Te´orica e Aplicada, 27(1) (2020), 83–91.
- [17] A. V. Panyukov and A. N. Tyrsin, Stable parametric identification of vibratory diagnostics objects, Journal of Vibroengineering, 10(2) (2008), 350.
- [18] A. Panyukov, T. Makarovskikh, and M. Abotaleb, Forecasting with Using Quasilinear Recurrence Equation, International Conference on Optimization and Applications, Springer, (2022), 183–195.
- [19] A. Panyukov, T. Makarovskikh, and M. Abotaleb, Forecasting with using quasilinear recurrence equation, International Conference on Optimization and Applications, Springer, (2022), 183–195.
- [20] A. V. Panyukov and Y. A. Mezaal, Improving of the identification algorithm for a quasilinear recurrence equation, Advances in Optimization and Applications: 11th International Conference, OPTIMA 2020, Moscow, Russia, September 28–October 2, 2020, Revised Selected Papers 11 , Springer, (2020), 15–26.
- [21] A. V. Panyukov and Y. A. Mezaal, Parametric identification of quasilinear difference equation, Vestn. YuzhnoUral. Gos. Un-ta. Ser. Matem. Mekh. Fiz., 11(4) (2019), 32–38.
- [22] A. V. Panyukov and others, Stable identification of linear autoregressive model with exogenous variables on the basis of the generalized least absolute deviation method, Bulletin of the South Ural State University. Series: Mathematical Modeling and Programming, Federal State Budgetary Educational Institution of Higher Education, 11(1) (2018), 35–43.
- [23] T. Makarovskikh, A. Panyukov, and M. Abotaleb, Using General Least Deviations Method for Forecasting of Crops Yields, International Conference on Mathematical Optimization Theory and Operations Research, Springer, (2023), 376–390.
- [24] J. Pan, H. Wang, and Q. Yao, Weighted least absolute deviations estimation for ARMA models with infinite variance, Econometric Theory, Cambridge University Press, 23(5) (2007), 852–879.
- [25] A. V. Panyukov and V. A. Golodov, Computing Best Possible Pseudo-Solutions to Interval Linear Systems of Equations, Reliable Computing, 19 (2013), 215–228.
- [26] A. V. Panyukov and Y. A. Mezaal, Stable estimation of autoregressive model parameters with exogenous variables on the basis of the generalized least absolute deviation method, IFAC-PapersOnLine, Elsevier, 51(11) (2018), 1666–1669.
- [27] R. Panchal and B. Kumar, Forecasting industrial electric power consumption using regression-based predictive model, Recent Trends in Communication and Electronics: Proceedings of the International Conference on Recent Trends in Communication and Electronics (ICCE-2020), Ghaziabad, India, 28-29 November, 2020, CRC Press, (2021), 135.
- [28] M. S. A. Abotaleb and T. Makarovskikh, The research of mathematical models for forecasting Covid-19 cases, International Conference on Mathematical Optimization Theory and Operations Research, Cham: Springer International Publishing, (2021), 301–315.
- [29] J. Manafian and M. Lakestani, N-lump and interaction solutions of localized waves to the (2+ 1)-dimensional variable-coefficient Caudrey–Dodd–Gibbon–Kotera–Sawada equation, Journal of Geometry and Physics, 150 (2020), 103598.
- [30] J. Manafian and M. Lakestani, Optical soliton solutions for the Gerdjikov–Ivanov model via tan(φ/2)-expansion method, Optik, 127(20) (2016), 9603–9620.
- [31] Y. Gu, S. Malmir, J. Manafian, O. A. Ilhan, A. A. Alizadeh, and A. J. Othman, Variety interaction between k-lump and k-kink solutions for the (3+ 1)-D Burger system by bilinear analysis, Results in Physics, 43 (2022), 106032.
- [32] N. H. Ali, S. A. Mohammed, and J. Manafian, Study on the simplified MCH equation and the combined KdV–mKdV equations with solitary wave solutions, Partial Differential Equations in Applied Mathematics, 9 (2024), 100599.
- [33] J. Manafian, L. A. Dawood, and M. Lakestani, New solutions to a generalized fifth-order KdV like equation with prime number p= 3 via a generalized bilinear differential operator, Partial Differential Equations in Applied Mathematics, 9 (2024), 100600.
- [34] M. Zhang, X. Xie, J. Manafian, O. A. Ilhan, and G. Singh, Characteristics of the new multiple rogue wave solutions to the fractional generalized CBS-BK equation, Journal of Advanced Research, 38 (2022), 131–142.
- [35] J. Manafian and M. Lakestani, Abundant soliton solutions for the Kundu–Eckhaus equation via tan(φ(ξ))expansion method, Optik, 127(14) (2016), 5543–5551.
- [36] J. Manafian and M. Lakestani, Application of tan(φ/2)-expansion method for solving the Biswas–Milovic equation for Kerr law nonlinearity, Optik, 127(4) (2016), 2040–2054.
- [37] M. Lakestani, J. Manafian, A. R. Najafizadeh, and M. Partohaghighi, Some new soliton solutions for the nonlinear the fifth-order integrable equations, Computational Methods for Differential Equations, 10(2) (2022), 445–460.
- [38] C. B. A. Satrio, W. Darmawan, B. U. Nadia, and N. Hanafiah, Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET, Procedia Computer Science, Elsevier, 179 (2021), 524– 532.
- [39] D. V. Sirotin, Neural network approach to forecasting the cost of ferroalloy production, Izvestiya Vysshikh Uchebnykh Zavedenii. Chernaya Metallurgiya, 63(1) (2020), 78–83.
- [40] F. E. H. Tay and L. Cao, Application of support vector machines in financial time series forecasting, Omega, Elsevier, 29(4) (2001), 309–317.
- [41] A. N. Tyrsin, Robust construction of regression models based on the generalized least absolute deviations method, Journal of Mathematical Sciences, Springer, 139 (2006), 6634–6642.
- [42] D. M. Yakubova, Econometric models of development and forecasting of black metallurgy of Uzbekistan, Asian Journal of Multidimensional Research (AJMR), TRANS Asian Research Journals, 8(5) (2019), 310–314.
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