Novel method in determining the nature of Univariate time series in forecasting Angola Gross Domestic Product (GDP)

Abstract

The main objective of this study is to use 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 Angola GDP. This data is the GDP yearly data of Angola gross domestic product (Angola GDP). The (Angola GDP) data spanned for the period of twenty years (2002 to 2022). The GDP of Angola is a secondary data obtained from the DataStream of Universiti Utara Malaysia 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. Angola GDP is sliding, improvement on GDP is urgently necessary or els it get to ruin. Improvement in Angola GDP is recommended.