Automated Detection Trend Breaks in the Identification of Ireland's Gross Domestic Product (GDP) for Accurate Forecasting

NONE 20260610115300248 wseas/wseas content-registration-form+ja@crossref.org content-registration-form International Journal of Computational and Applied Mathematics & Computer Science 2769-2477 Automated Detection Trend Breaks in the Identification of Ireland's Gross Domestic Product (GDP) for Accurate Forecasting Olubunmi Temitope Olorunpomi Department of Statistics Federal University Lokoja P.M.B. 1154, Lokoja, Kogi State NIGERIA Ajare Emmanuel Oloruntoba Department of Statistics Federal University Lokoja P.M.B. 1154, Lokoja, Kogi State NIGERIA Hafsat Olaide Salah Department of Library and Information Science University of Abuja P.M.B. 117, Abuja NIGERIA The main objective of this study is to use automated forecasting tools in identification of Ireland Gross Domestic Product (GDP). Accurate GDP forecasting is essential for effective economic planning and policy formation. It enables policymakers to anticipate economic downturns and plan accordingly to mitigate their impacts. Economists benefit significantly from improved forecasting models as they provide deeper insights into the underlying economic dynamics. Ireland's economy has experienced several structural shifts over the past few decades. BFAST (Break for Additive, Season and Trend) to identify the components of time series present in the seasonal data of Gross Fixed Capital Formation know as Gross Domestic Product of Ireland GDP. This data is the GDP yearly data of Ireland gross domestic product (Ireland land GDP). The (Ireland GDP) data spanned for the period of thirteen years (2010 to 2022) then 2023 and 2024 is used for data training. The GDP of Ireland is a secondary data obtained from the DataStream of National University Singapore Library. The BFAST (Break for Additive Seasonal and Trend) was utilized to identify the time series components. BFAST only identifies trend and seasonal components while considering all other components as random. Empirical data were employed to BFAST and subsequently determine the next forecasting technique after which forecast is made ahead. The real data findings suggested that BFAST can provide a better time series components identification better than manual process and hence caution should be taken serious. Ireland GDP is sliding, improvement on GDP is urgently necessary or else it get to ruin. Improvement in Iceland GDP is recommended. 06 10 2026 06 10 2026 1 1 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/232028.2026.6.1 https://wseas.com/journals/camcs/2026/a02camcs-001(2026).pdf Ajare, E.O. & Ism ail, S. (2 019). Break for Time Series Co mponents (BFTSC) and Group for Tim e Series Co mponents (GFTSC) in Identification of Time Series Components in Univa riate Forecasting. Advanced Research in Dynam ical and Control Systems, Volume 11, 05-Sp ecial Issue 2019, Pag es: 995-1004. http://www.jardcs.org/special-issue.php. Ajare, E.O., & Ismail, S. 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