Automated Detection of Structural Change in Ethopia Gross Domestic Product (GDP) using Novel Algorithm

Abstract

The target of this study is to use GFTSC (Group for time series modules/components) to classify the constituents components of time series existing in the Ethiopia Gross Domestic Product (GDP). This statistics is the GDP yearly data of Ethiopian Gross Domestic Product (GDP). The Gross fixed capital formation (% of GDP) was available. The (Ethiopia GDP) data for the period of twelve years. The GDP of Ethiopia is a secondary data obtained from the DataStream of National University Singapore Library.  The softness of BFAST (Break for Additive/multiplicative Seasonal and Trend) were inspected by the extension of BFAST to GFTSC. GFTSC was created to involve the cyclical and irregular constituents that was not involved by BFAST technique. GFTSC is aimed to synchronous the image of all the 4 time series constituents. Experiential statistics of Ethiopia was employed to GFTSC and subsequently the next forecast was made. The simulated and real data findings suggested that BFTSC can provide a better time series components identification better than manual process and hence caution should be taken because Ethiopia GDP had only stationery trend, hence not really improving and not dropping, so caution should be taken  less it got to ruin. Improvement in Ethiopia GDP is recommended.