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VOL. 13, ISSUE 2 (2026)
Identifying change and forecasting in mauritania Gross Domestic Product (GDP) using Advance automated technique
Authors
Ajare Emmanuel Oloruntoba, Hafsat Olaide Salah, Olubunmi Temitope Olorunpomi, Ajare Aduragba Oluwasegun, Uko Peter Edidiong
Abstract
The main objective of this study is to use BFAST
(Breaks for Additive Seasonal and Trend) to identify the components of time
series present in the Mauritania Gross Domestic Product (GDP). This data is the
GDP yearly data of Mauritania Gross Domestic Product (GDP). The Gross fixed
capital formation (% of GDP) was provided. The (Mauritania GDP) data spanned
for the period of sixty three years. The GDP of Mauritania is a secondary data
obtained from the DataStream of Universiti Utara Malaysia Library. BFAST was
designed to present the image of all the 3 time series components. BFAST only
identifies trend and seasonal components only. Empirical data of Mauritania was
employed to BFAST and subsequently the next forecast was made. The simulated
and real data findings suggested that BFAST can provide a better time series
components identification better than manual process and hence caution should
be taken because Mauritania GDP is sliding, less it got to ruin. Improvement in
Mauritania GDP is recommended.
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Pages:25-30
How to cite this article:
Ajare Emmanuel Oloruntoba, Hafsat Olaide Salah, Olubunmi Temitope Olorunpomi, Ajare Aduragba Oluwasegun, Uko Peter Edidiong "Identifying change and forecasting in mauritania Gross Domestic Product (GDP) using Advance automated technique". International Journal of Multidisciplinary Research and Development, Vol 13, Issue 2, 2026, Pages 25-30
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