Detecting trend breaks and forecasting ice-land Gross Domestic Product (GDP) using automated technique

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 Ice Land GDP. This data is the GDP yearly data of Ice Land gross domestic product (Ice Land GDP). The (Ice Land GDP) data spanned for the period of fifty three years (1970 to 2022). The GDP of Ice Land 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. Ice Land GDP is sliding, improvement on GDP is urgently necessary or else it get to ruin. Improvement in Ice Land GDP is recommended.