Home Business & Economics Trend and Fuzzy Time Series Analysis of Live Births Registration in Northern Ghana
Article
Licensed
Unlicensed Requires Authentication

Trend and Fuzzy Time Series Analysis of Live Births Registration in Northern Ghana

  • Abdulai Nagumsi , Suleman Nasiru EMAIL logo , Abdul-Aziz Adam Kobilla and Mohammed Hashim Bamba Mustapha
Published/Copyright: February 20, 2024
Become an author with De Gruyter Brill

Abstract

This study investigated four trend analysis models namely; linear, quadratic, semi log linear and semi log quadratic to study the pattern of live births registration in the Northern Region of Ghana. The study revealed that Semi log linear trend model is the best trend model for studying the pattern of live births registration in the Northern Region of Ghana based on AIC and BIC criteria. The study further fitted four existing fuzzy time series (FTS) models for forecasting live births registration in the Northern Region of Ghana. The Chen, Singh, Heuristic and Chen-Hsu models are the four models used to analyze the data.


Corresponding author: Suleman Nasiru, Department of Statistics and Actuarial Science, School of Mathematical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana, E-mail:

  1. Research funding: We declare that we have not received any funding from any agency for engaging in this research.

  2. Conflict of interest: We hereby declare that there are no conflict of interest regarding the publication of this work.

References

Abbasov, A. M., and M. H. Mamedova. 2003. “Application of Fuzzy Time Series to Population Forecasting.” In Proceedings of the 8th Symposion on Information Technology in Urban and Spatial Planning, 545–552. Vienna, Austria: Vienna University of Technology.Search in Google Scholar

Akomolafe, A., O. Adeoti, C. Awogbemi, A. Aliu, and A. Bagbe. 2021. “Live Birth Registrations in Nigeria: Analytical Approach Using Arima Model.” African Journal of Mathematics and Statistics Studies 4 (1): 63–74.Search in Google Scholar

Amo-Adjei, J., and S. K. Annim. 2015. “Socioeconomic Determinants of Birth Registration in ghana.” BMC International Health and Human Rights 15: 1–9. https://doi.org/10.1186/s12914-015-0053-z.Search in Google Scholar

Baidoo, S. 2012. “How Can ICTS and New/Social Media Remedy the Problem of Vital Statistics Deficiencies in Ghana? (The Case of Ghana Births and Deaths Registry Department).” Master of Art Thesis. University of Malmö Sweden, (Unpublished).Search in Google Scholar

Bhatia, A., L. Z. Ferreira, A. J. Barros, and C. G. Victora. 2017. “Who and where Are the Uncounted Children? Inequalities in Birth Certificate Coverage Among Children under Five Years in 94 Countries Using Nationally Representative Household Surveys.” International Journal for Equity in Health 16 (1): 1–11. https://doi.org/10.1186/s12939-017-0635-6.Search in Google Scholar

Chen, C. 1996. “Regional Determinants of Foreign Direct Investment in Mainland china.” Journal of Economics Studies 23 (2): 18–30. https://doi.org/10.1108/01443589610109649.Search in Google Scholar

Chen, S. M., and C. C. Hsu. 2004. “A New Method to Forecast Enrollments Using Fuzzy Time Series.” International Journal of Applied Science & Engineering 2 (3): 234–44.Search in Google Scholar

Fatih, C. 2022. “Forecasting Models Based on Fuzzy Logic: An Application on International Coffee Prices.” Econometrics 26 (4): 1–16. https://doi.org/10.15611/eada.2022.4.01.Search in Google Scholar

Gayawan, E., S. B. Adebayo, A. A. Komolafe, and A. A. Akomolafe. 2019. “Spatial Distribution of Malnutrition Among Children under Five in Nigeria: A Bayesian Quantile Regression Approach.” Applied Spatial Analysis and Policy 12: 229–54. https://doi.org/10.1007/s12061-017-9240-8.Search in Google Scholar

Gerber, P., A. Gargett, and M. Castan. 2011. “Does the Right to Birth Registration Include a Right to a Birth Certificate?” Netherlands Quarterly of Human Rights 29 (4): 434–59. https://doi.org/10.1177/016934411102900403.Search in Google Scholar

Huarng, K. 2001. “Heuristic Models of Fuzzy Time Series for Forecasting.” Fuzzy Sets and Systems 123 (3): 369–86. https://doi.org/10.1016/s0165-0114(00)00093-2.Search in Google Scholar

Karunanidhi, D., and S. Sasikala. 2023. “Robustness of Predictive Performance of Arima Models Using Birth Rate of Tamilnadu.” Journal of Statistics Applications and Probability 12 (3): 1189–201.10.18576/jsap/120326Search in Google Scholar

Kemalbay, G., and O. B. Korkmazoglu. 2021. “Sarima-arch versus Genetic Programming in Stock Price Prediction.” Sigma Journal of Engineering and Natural Sciences 39 (2): 110–22. https://doi.org/10.14744/sigma.2021.00001.Search in Google Scholar

Lucas, P. O., O. Orang, P. C. L. Silva, E. M. A. M. Mendes, and F. G. Guimarães. 2021. “A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges.” Learning and Nonlinear Models – Journal of the Brazilian Society on Computational Intelligence (SBIC) 19 (3): 29–50. https://doi.org/10.21528/lnlm-vol19-no2-art3.Search in Google Scholar

Ojiako, I. A., and G. Olayode. 2008. “Analysis of Trends in Livestock Production in Nigeria: 1970–2005.” Journal of Agriculture and Social Research 8 (1): 114–20. https://doi.org/10.4314/jasr.v8i1.2892.Search in Google Scholar

Orenstein, J. 2009. “Being Nobody-The Difficulties Faced by Aboriginal Victorians in Obtaining Identification.” In Conference Paper, NACLC Conference Perth, Vol. 18.Search in Google Scholar

Silva, P. C., Sadaei, H. J., and Guimaraes, F. G. (2016). Interval Forecasting with Fuzzy Time Series. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1–8. IEEE.10.1109/SSCI.2016.7850010Search in Google Scholar

Singh, S. R. 2008. “A Computational Method of Forecasting Based on Fuzzy Time Series.” Mathematics and Computers in Simulation 79 (3): 539–54. https://doi.org/10.1016/j.matcom.2008.02.026.Search in Google Scholar

SlothNielsen, J., and R. SlothNielsen. 2020. “Mothers and Others: Transgender Birth, Birth Registration and the Rights of the Child, with a Focus on the united kingdom and south africa.” International Journal of Discrimination and the Law 20 (4): 203–23. https://doi.org/10.1177/1358229120970142.Search in Google Scholar

Song, Q., and B. S. Chissom. 1993. “Forecasting Enrollments with Fuzzy Time Series—Part I.” Fuzzy Sets and Systems 54 (1): 1–9. https://doi.org/10.1016/0165-0114(93)90355-l.Search in Google Scholar

Song, Q., and B. S. Chissom. 1994. “Forecasting Enrollments with Fuzzy Time Series—Part II.” Fuzzy Sets and Systems 62 (1): 1–8. https://doi.org/10.1016/0165-0114(94)90067-1.Search in Google Scholar

Received: 2023-09-21
Accepted: 2024-01-24
Published Online: 2024-02-20
Published in Print: 2024-03-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 29.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/spp-2023-0034/html
Scroll to top button