Group for Time Series Components (GFTSC) Identification of Gross Domestic Product (GDP) of United Kingdom (UK)

Authors : Adefabi Adekunle; Ajare Emmanuel Oloruntoba

Volume/Issue : Volume 8 - 2023, Issue 7 - July

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The main objective of this study is to use GFTSC (Group for Time Series Components) to identify the components of time series present in the seasonal data of (UK GDP). This data is the GDP yearly data of United Kingdom gross domestic product (UK GDP). The (UK GDP) data spanned for the period of twenty years. The GDP of UK is a secondary data obtained from the DataStream of Universiti Utara Malaysia Library. The weaknesses of BFAST (Break for Additive Seasonal and Trend) were corrected by the extension of BFAST to GFTSC which resulted into creation of a new technique named Group for Time Series Components. BFTSC was created to capture the cyclical and irregular components that was not captured by BFAST technique. BFTSC is designed to present the image of all the 4 time series components. BFAST only identifies trend and seasonal components only. Evaluation using simulation data was conducted to verify the accuracy of GFTSC using monthly simulated of 144, 000 data unit. This data contained 48 months small monthly sample size, 96 monthly medium sample size, 144 months large sample size. Each of the sample size was replicated 100 time each. GFTSC is effective and better than BFAST because it was able to identify approximately 100% of the data with the basic four time series components monthly. BFTSC detects 99.99% of the entire components in the time series monthly data that was tested. Empirical data were employed to BFTSC and subsequently determine the next forecasting technique after which one step forecast is made ahead. The simulated and real data findings suggested that GFTSC can provide a better alternative to BFAST technique, hence GFTSC is recommended.

Keywords : Group for Time Series Components, Seasonal Data, Gross, Cyclical , Irregular Components.


Paper Submission Last Date
30 - November - 2023

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