Automated Detection of Structural Change in Botswana Gross Domestic Product (GDP) using Novel Algorithm. (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2020)
The main objective of this study is to examine the phenomenon, Structural change in the gross domestic product of Botswana and possible forecast and recommend for future adjustment. Gross fixed capital formation (% of GDP). Singapore Gross Domestic Product (GDP) data   and some resource materials were obtained from the data stream of Universiti Utara Malaysia and National University Singapore library.  In the methodology, BFAST (Break for additive, Season and trend) and BFTSC (Break for time series components) was used to examine the structural change,  time series components present in the data  (Botswana GDP) using R and Python software. BFTSC was created to capture the trend, seasonal, cyclical and irregular components as a combined image and to present them in a single plot.  The result obtained, model acquired from the components (pattern) extracted using BFTSC was subsequently process for forecasting purposes and recommendation follows. The real data findings suggested that BFTSC can provide a better time series components identification better than manual process and hence should be taken serious. Botswana GDP is growing declining, caution should be taken to improve the GDP such that Botswana can have bounty reserve and can also loan poor and financial weak countries. Improvement Botswana GDP is recommended.