Automated Detection of Structural Change in Ethopia Gross Domestic Product (GDP) using Novel Algorithm
Ajare Emmanuel Oloruntoba *
School of Quantitative Sciences, College of Art and Sciences, University Utara Malaysia, Sintok, Malaysia, Department of Mathematical Sciences, Faculty of Sciences, Federal University Gusau, Gusau, Nigeria and Department of Mathematics and Statistics, Austin Peay State University, Clarksville, Tennessee, USA.
Shobanke Dolapo Abidemi
Federal University Lokoja, Nigeria.
Adeyemo Abiodun
Department of Statistics, University of Abuja, Nigeria.
Adefabi Adekunle
School of Quantitative Sciences, College of Art and Sciences, University Utara Malaysia, Sintok, Malaysia and Department of Mathematics and Statistics, Austin Peay State University, Clarksville, Tennessee, USA.
*Author to whom correspondence should be addressed.
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.
Keywords: Ethiopia, break for time series components, seasonal data, gross, recurrent, asymmetrical components