Forecasting the Number of Patients with Kidney Failure in Thi-Qar Governorate using Time Series

  • Hassan Hopoop Razaq College of Administration and Economics, Department of Economics, University of Thi –Qar
Keywords: Autoregressive Model, Time Series Analysis, Mean Squares Error, ARIMA Model


This paper has been used time series models for the study and analysis of monthly data to the number of patients with Kidney Failure in Thi-Qar Province for the period (2020-2023) in order forecasting by the numbers of patients with Kidney Failure for the period (2024-2025). The result of data analysis show that the proper and suitable model is Integrated Autoregressive model of order ARIMA (4, 0, 1) because it has the least mean squares error (MSE). Based on the best model, the number of people with Kidney failure was predicted monthly and for the next two years and the predictive value was consistent with the original values and this indicates the efficiency of the model.


A. C. Harvey and N. Shephard, 10 Structural time series models. Elsevier, 1993. [Online]. Available:

A. Zellner and F. Palm, “Time series analysis and simultaneous equation econometric models,” J Econom, 1974, [Online]. Available:

F. Palm, “Time series analysis and simultaneous equation models with macroeconomic applications,” Co-promoteur avec A. Zellner). Professeur, Universiteit …, 1975.

C. W. J. Granger and M. J. Morris, “Time series modeling and interpretation,” … , Seasonality, Nonlinearity, Methodology, and …, 2001, doi: 10.5555/766886.766898.

F. Harris and R. M. Gwier, “A receiver structure that performs simultaneous spectral analysis and time series channelization,” Proceedings of the SDR’09 Technical Conference and …, 2009.

G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, Time series analysis: forecasting and control., 2015. [Online]. Available:

M. G. Dekimpe and D. M. Hanssens, “Time-series models in marketing:: Past, present and future,” International journal of research in marketing, 2000, [Online]. Available:

B. Lim and S. Zohren, “Time-series forecasting with deep learning: a survey,” … Transactions of the Royal Society A, 2021, doi: 10.1098/rsta.2020.0209.

S. L. Zeger, R. Irizarry, and R. D. Peng, “On time series analysis of public health and biomedical data,” Annu. Rev. Public Health, 2006, doi: 10.1146/annurev.publhealth.26.021304.144517.

R. H. Sumway and D. S. Stoffer, “Time series analysis and its applications with R examples,” Time series analysis and its applications with R …, 2006.

T. W. Anderson, The statistical analysis of time series., 2011. [Online]. Available:

A. Lugmayr, “RePaint: Inpainting using Denoising Diffusion Probabilistic Models,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022, pp. 11451–11461, 2022, doi: 10.1109/CVPR52688.2022.01117.

F. Chien, “The role of renewable energy and urbanization towards greenhouse gas emission in top Asian countries: Evidence from advance panel estimations,” Renew Energy, vol. 186, pp. 207–216, 2022, doi: 10.1016/j.renene.2021.12.118.

A. Mujtaba, “Symmetric and asymmetric impact of economic growth, capital formation, renewable and non-renewable energy consumption on environment in OECD countries,” Renewable and Sustainable Energy Reviews, vol. 160, 2022, doi: 10.1016/j.rser.2022.112300.

T. S. Adebayo, “Role of country risks and renewable energy consumption on environmental quality: Evidence from MINT countries,” J Environ Manage, vol. 327, 2023, doi: 10.1016/j.jenvman.2022.116884.

S. Gu, “Vector Quantized Diffusion Model for Text-to-Image Synthesis,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022, pp. 10686–10696, 2022, doi: 10.1109/CVPR52688.2022.01043.

Y. Ding, “Semi-Supervised Locality Preserving Dense Graph Neural Network with ARMA Filters and Context-Aware Learning for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, 2022, doi: 10.1109/TGRS.2021.3100578.

Q. Wang, “Underestimated impact of the COVID-19 on carbon emission reduction in developing countries – A novel assessment based on scenario analysis,” Environ Res, vol. 204, 2022, doi: 10.1016/j.envres.2021.111990.

H. Tao, “Groundwater level prediction using machine learning models: A comprehensive review,” Neurocomputing, vol. 489, pp. 271–308, 2022, doi: 10.1016/j.neucom.2022.03.014.

G. J. Qi, “Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods,” IEEE Trans Pattern Anal Mach Intell, vol. 44, no. 4, pp. 2168–2187, 2022, doi: 10.1109/TPAMI.2020.3031898.

Z. Xu, “Using econometric and machine learning models to forecast crude oil prices: Insights from economic history,” Resources Policy, vol. 83, 2023, doi: 10.1016/j.resourpol.2023.103614.

M. Sikder, “The integrated impact of GDP growth, industrialization, energy use, and urbanization on CO2 emissions in developing countries: Evidence from the panel ARDL approach,” Science of the Total Environment, vol. 837, 2022, doi: 10.1016/j.scitotenv.2022.155795.

B. Wu, “A social-ecological coupling model for evaluating the human-water relationship in basins within the Budyko framework,” J Hydrol (Amst), vol. 619, 2023, doi: 10.1016/j.jhydrol.2023.129361.

S. Kumari, “Machine learning-based time series models for effective CO2 emission prediction in India,” Environmental Science and Pollution Research, vol. 30, no. 55, pp. 116601–116616, 2023, doi: 10.1007/s11356-022-21723-8.

H. M. Rasel, “Sustainable futures in agricultural heritage: Geospatial exploration and predicting groundwater-level variations in Barind tract of Bangladesh,” Science of the Total Environment, vol. 865, 2023, doi: 10.1016/j.scitotenv.2022.161297.

E. G. Kim, “Designing solar power generation output forecasting methods using time series algorithms,” Electric Power Systems Research, vol. 216, 2023, doi: 10.1016/j.epsr.2022.109073.

G. Xiaomin, “How does urbanization affect energy carbon emissions under the background of carbon neutrality?,” J Environ Manage, vol. 327, 2023, doi: 10.1016/j.jenvman.2022.116878.

J. Wei, “Ultra-short-term forecasting of wind power based on multi-task learning and LSTM,” International Journal of Electrical Power and Energy Systems, vol. 149, 2023, doi: 10.1016/j.ijepes.2023.109073.

P. Bórawski, “Perspectives of photovoltaic energy market development in the european union,” Energy, vol. 270, 2023, doi: 10.1016/

J. Kaur, “Autoregressive models in environmental forecasting time series: a theoretical and application review,” Environmental Science and Pollution Research, vol. 30, no. 8, pp. 19617–19641, 2023, doi: 10.1007/s11356-023-25148-9.

Bharti, “Short-term traffic flow prediction based on optimized deep learning neural network: PSO-Bi-LSTM,” Physica A: Statistical Mechanics and its Applications, vol. 625, 2023, doi: 10.1016/j.physa.2023.129001.

M. Ali, “Prediction of Complex Stock Market Data Using an Improved Hybrid EMD-LSTM Model,” Applied Sciences (Switzerland), vol. 13, no. 3, 2023, doi: 10.3390/app13031429.
How to Cite
Razaq, H. H. (2024). Forecasting the Number of Patients with Kidney Failure in Thi-Qar Governorate using Time Series. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 5(2), 24-35. Retrieved from