DOI : https://doi.org/10.18517/ijaseit.15.3.12338

Integrating Statistical Models for Forecasting Nonlinear Time Series

Aseel Abdulrazzak Rasheed (1), Rawaa Salh AL-Saffar (2), Asia Hammood Husssein (3)
(1) Statistics Department - College of Administration and Economics, Mustansiriyah University, Baghdad, Iraq
(2) Statistics Department - College of Administration and Economics, Mustansiriyah University, Baghdad, Iraq
(3) Statistics Department - College of Administration and Economics, Mustansiriyah University, Baghdad, Iraq
Fulltext View | Download
How to cite (IJASEIT) :
[1]
A. A. Rasheed, R. S. AL-Saffar, and A. H. Husssein, “Integrating Statistical Models for Forecasting Nonlinear Time Series”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 3, pp. 799–805, Jun. 2025.
Citation Format :
The issue of unemployment is one of the most prominent challenges facing the growth and development of the Iraqi economy due to its profound repercussions on the economic and social conditions. What exacerbates the severity of the unemployment problem is its prolonged existence and recurring nature, as well as its manifestation in various forms, including disguised unemployment, over recent years. It is noteworthy that unemployment is no longer limited to uneducated or moderately educated youth, but has also extended to those with higher degrees. Although the state has taken the initiative to employ youth with educational degrees, unemployment rates have not shown an apparent decrease due to the large number of unemployed people in Iraq. In this research, the best model was chosen from among the non-linear time series models to predict unemployment rates in Iraq, which included the Logistic, Gompertz, and Chapman-Richard models. It was found that the best model for predicting unemployment rates in Iraq is the Chapman-Richard model. Consequently, we suggest leveraging this research to develop future strategies that address the unemployment issue in Iraq and expand job opportunities in both the public and private sectors to meet the growing number of unemployed individuals. We propose applying the best model from this study to explore other economic and social issues. We advise enhancing efforts to accurately gather and record data so that it can be utilized by researchers in subsequent studies.

H. T. Abd, H. A. Rasheed, and A. A. Rasheed, "An examination of the impulse response function under configuration of the multiple autoregressive," Ind. Eng. Manag. Syst., vol. 23, no. 3, pp. 300–310, 2024, doi: 10.7232/iems.2024.23.3.300.

E. Bradley and H. Kantz, "Nonlinear time-series analysis revisited," Chaos, vol. 25, no. 9, 2015, doi:10.1063/1.4917289.

B. M. Lafta and A. A. Rasheed, "Comparing some adjacency matrices to estimate a spatial negative binomial regression model," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 189–195, Feb. 2025, doi:10.18517/ijaseit.15.1.12554.

Y. Liu, S. Zhang, X. Chen, and J. Wang, "Artificial combined model based on hybrid nonlinear neural network models and statistics linear models-research and application for wind speed forecasting," Sustainability, vol. 10, no. 12, 2018, doi:10.3390/su10124601.

D. Fan, H. Sun, J. Yao, K. Zhang, X. Yan, and Z. Sun, "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, vol. 220, 2021, doi:10.1016/j.energy.2020.119708.

A. K. Singh et al., "An integrated statistical-machine learning approach for runoff prediction," Sustainability, vol. 14, no. 13, 2022, doi:10.3390/su14138209.

A. J. Clark, L. W. Lake, and T. W. Patzek, "Production forecasting with logistic growth models," in Proc. SPE Annu. Tech. Conf. Exhib., Oct. 2011, doi: 10.2118/144790-MS.

A. H. Gatea, N. J. Sadik, and H. A. Rasheed, "Modeling the trend of Iraqi GDP for 1970-2020," Period. Eng. Nat. Sci., vol. 11, no. 1, pp. 216–222, 2023, doi: 10.21533/pen.v11i1.3455.

R. Huffaker, M. Bittelli, and R. Rosa, Nonlinear Time Series Analysis with R. Oxford, U.K.: Oxford Univ. Press, 2017, doi:10.1093/oso/9780198782933.003.0001.

D. A. Ratkowsky, "Statistical properties of alternative parameterizations of the von Bertalanffy growth curve," Can. J. Fish. Aquat. Sci., vol. 43, no. 4, pp. 742–747, 1986, doi: 10.1139/f86-091.

T. Reddy et al., "Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: A data driven approach," BMC Med. Res. Methodol., vol. 21, pp. 1–11, 2021, doi:10.1186/s12874-020-01165-x.

F. J. Richards, "A flexible growth function for empirical use," J. Exp. Bot., vol. 10, no. 2, pp. 290–301, 1959, doi: 10.1093/jxb/10.2.290.

K. M. Tjørve and E. Tjørve, "The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family," PLOS ONE, vol. 12, no. 6, 2017, doi:10.1371/journal.pone.0178691.

D. Kurisu, R. Fukami, and Y. Koike, "Adaptive deep learning for nonlinear time series models," Bernoulli, vol. 31, no. 1, pp. 240–270, 2025, doi: 10.3150/24-BEJ1726.

C. V. Trappey and H. Y. Wu, "An evaluation of the time-varying extended logistic, simple logistic, and Gompertz models for forecasting short product lifecycles," Adv. Eng. Informat., vol. 22, no. 4, pp. 421–430, 2008, doi: 10.1016/j.aei.2008.05.007.

U. Ulfa et al., "Feasibility study of grid connected solar PV design with battery at Sandai 150/20 kV substation as a Tayap 4 feeder backup when system blackout occurs," Int. J. Comput. Eng., vol. 1, no. 4, pp. 119–125, 2024, doi: 10.62527/comien.1.4.28.

S. Kehinde et al., "A commentary on integrating green technology into civil engineering: Innovative approaches for sustainable infrastructure development in urban areas," Int. J. Comput. Eng., vol. 1, no. 4, pp. 126–128, 2024, doi: 10.62527/comien.1.4.29.

H. Santoso, G. Ferreira Soares, and C. M. Angelo, "Detection of children's facial expressions on the effects of playing games using CNN algorithm," Int. J. Adv. Sci. Comput. Eng., vol. 6, no. 3, pp. 152–157, 2024, doi: 10.62527/ijasce.6.3.213.

N. Suico et al., "Vouching the digital literacy in instruction viz-a-viz performance: Contextualized enhancement activities," Int. J. Adv. Sci. Comput. Eng., vol. 6, no. 3, pp. 138–151, 2024, doi:10.62527/ijasce.6.3.198.

R. Kumar, Y. A. Lad, and P. Kumari, "Forecasting potato prices in Agra: Comparison of linear time series statistical vs. neural network models," Potato Res., 2025, doi: 10.1007/s11540-024-09838-6.

W. Zhang, Z. Lin, and X. Liu, "Short-term offshore wind power forecasting-A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Term Memory (LSTM)," Renew. Energy, vol. 185, pp. 611-628, 2022, doi:10.1016/j.renene.2021.12.100.

K. E. ArunKumar et al., "Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends," Alexandria Eng. J., vol. 61, no. 10, pp. 7585-7603, 2022, doi:10.1016/j.aej.2022.01.011.

B. A. Sadkhan, S. H. Omran, and H. K. Sharaf, "An experimental analysis on the impact of the epoxy on the torsional behavior of composite fiber-glass," J. Adv. Res. Appl. Mech., vol. 117, no. 1, pp. 150-160, May 2024, doi: 10.37934/aram.117.1.150160.

I. O. B. Al-Fahad and H. K. Sharaf, "Investigation of the effect of heat transfer during friction stir welding (FSW) of AZ80A Mg alloy plates using a pin tool by conducting finite elements analysis," J. Adv. Res. Fluid Mech. Therm. Sci., vol. 117, no. 1, pp. 98-108, 2024, doi:10.37934/arfmts.117.1.98108.

A. Fattahi, H. K. Sharaf, and N. Mariah, "Thermal comfort assessment of UPM Engineering Library in tropical climate conditions," J. Adv. Res. Appl. Mech., vol. 117, no. 1, pp. 179-189, 2024, doi:10.37934/aram.117.1.179189.

H. D. Salman et al., "Comparative study on the creep behavior of a hybrid composite material doped with nano material," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 14, no. 6, pp. 1951-1956, Dec. 2024, doi:10.18517/ijaseit.14.6.15958.

A. T. Shomran et al., "Computational investigation on the fatigue behavior of titanium alloy Ti-6Al-2Sn-4Zr-2Mo under dynamic loads by consideration of ambient temperature," Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 1, pp. 75-80, Feb. 2025, doi:10.18517/ijaseit.15.1.12417.

F. T. Al-Maliky and J. S. Chiad, "Study and analysis the flexion moment in active and passive knee prosthesis using back propagation neural network predictive," J. Braz. Soc. Mech. Sci. Eng., vol. 44, 2022, doi: 10.1007/s40430-022-03850-y.

R. F. Ghazi, J. S. Chiad, and F. M. Abdulghani, "Design and manufacturing a smart shoe for diabetic foot ulcer monitoring and prediction system using internet-of-things technology," J. Braz. Soc. Mech. Sci. Eng., vol. 46, 2024, doi: 10.1007/s40430-023-04591-2.

N. K. Al-Hayali, S. M. Nacy, J. S. Chiad, and O. Hussein, "Analysis and evaluation of a quasi-passive lower limb exoskeleton for gait rehabilitation," Al-Khwarizmi Eng. J., vol. 17, no. 4, pp. 36-47, 2021, doi: 10.22153/kej.2021.12.007.

N. J. Khalaf et al., "Experimental investigation to optimize the manufacturing parameters of ankle-foot orthoses using composite and titanium nanoparticles," J. Compos. Sci., vol. 8, no. 2, 2024, doi:10.3390/jcs8020045.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).