Abstract

The main reason for this study is to use manual process of identification of time series components with two types of automated decomposition detection algorithm known as automated BFTSC (break for time series components) and, automated GFTSC (Group for time series components) in detecting change in a highly volatile curve London Stock Exchange (LSE) Data. Identifying the components of time series present in the data of London Stock Exchange (LSE). London Stock Exchange monthly data for 20 years (January 2001 until December 2020) was utilized in this study and obtained from Yahoo finance (yahoo-link). The London Stock Exchange (LSE) data is also available as a secondary data at the DataStream of Universiti Utara Malaysia Library. The weaknesses of BFAST were corrected by the extension of BFAST to BFTSC and GFTSC. Both were created to capture the cyclical and irregular components that was not captured by BFAST technique and it was included in the methodology of this study. BFTSC and GFTSC were designed to give a combined image of all the four time series components captured in a single time plot. Evaluation using simulation data was conducted in the past studies to verify the accuracy of both techniques of which both techniques are effective and better than BFAST because it was able to identify 100% of the data with the basic four time series components monthly. both techniques detects 99.97% of the entire components in the time series monthly data that was tested. The subsequently forecasting technique was determined. Lastly, some weakness of BFAST and GFTSC was highlighted in this paper in term of polynomial and cubic time series components identification.

Description