Comparing ARFIMA and ARIMA models in forecasting under five mortality rate in Tanzania
| dc.contributor.author | Mwijalilege, Sadock Aron | |
| dc.contributor.author | Kadigi, Michael Lucas | |
| dc.contributor.author | Kibiki, Castory | |
| dc.date.accessioned | 2025-07-11T18:11:32Z | |
| dc.date.available | 2025-07-11T18:11:32Z | |
| dc.date.issued | 2025-01-15 | |
| dc.description | Journal article | |
| dc.description.abstract | Tanzania has been taking various measures to drop the Under-Five Mortality Rate (UFMR), but the pace to meet national and global UFMR targets has been slow. Nevertheless, the decline for the past years has continued to be low as compared to the Sustainable Development Goals (SDGs) target which is set at 25 deaths/1000 live births by 2030. The lack of statistical modeling-based forecast values of UFMR results into setting targets that are not SMART towards the realization of national and international goals of the health sector. Thus, the current study uses both ARFIMA and ARIMA to make forecasts of UFMR in Tanzania from 2021 to 2030 by using data extracted from the World Databank - World Development Indicators (WDI). Also, an accuracy comparison between the ARFIMA and ARIMA best-fit models in forecasting UFMR was conducted. The forecasts from the best ARFIMA (1, 0.284243, 2) model indicate that by June 2026 the rate will on average be 41 deaths/1,000 live births as compared to the Tanzanian Five Year Development Plan Phase III (TFYDP-III) target of 40 deaths/1,000 live births; whereas the best fit ARIMA (1, 2, 0) model forecasts depict that the rate will be 40.1 deaths/1,000 live births as compared to the TFYDP-III target. In relation to the UN SDGs target of 25 deaths/1,000 live births by 2030, the ARFIMA (1, 0.284243, 2) model forecast values indicate that by 2030, Tanzania will experience a decrease in UFMR to 35.2 deaths/1,000 live births. The ARIMA (1, 2, 0) forecast values indicate that by 2030, Tanzania will experience a decrease in UFMR to 32.9 deaths/1,000 live births. The results of using RMSE and MAPE forecasting model accuracy measures reveal that the ARFIMA (1, 0.284243, 2) model performs better than ARIMA (1, 2, 0) in forecasting UFMR. | |
| dc.identifier.citation | Mwijalilege, Sadock Aron, Michael Lucas Kadigi, and Castory Kibiki. 2025. “Comparing ARFIMA and ARIMA Models in Forecasting under Five Mortality Rate in Tanzania”. Asian Journal of Probability and Statistics 27 (1):107-21. https://doi.org/10.9734/ajpas/2025/v27i1707. | |
| dc.identifier.issn | 2582-0230 | |
| dc.identifier.uri | https://doi.org/10.9734/ajpas/2025/v27i1707 | |
| dc.identifier.uri | https://www.suaire.sua.ac.tz/handle/123456789/6823 | |
| dc.language.iso | en | |
| dc.publisher | Asian Journal of Probability and Statistics | |
| dc.subject | Forecasting | |
| dc.subject | mortality rate | |
| dc.subject | integrated moving average | |
| dc.subject | Autoregressive | |
| dc.subject | ARFIMA | |
| dc.subject | public health | |
| dc.title | Comparing ARFIMA and ARIMA models in forecasting under five mortality rate in Tanzania | |
| dc.type | Article |