Skip to content
Research Article Open access CC BY 4.0

Detecting Change in a Volatile Curve United State Stock Market (US SM) with the Use of Automated Decomposition for Time Series Components

Ajare Emmanuel Oloruntoba, Adefabi Adekunle, Olorunpomi Temitope Olubunmi

Asian Journal of Research in Computer Science · pp. 74–84 · Published 6 Mar 2024

10.9734/ajrcos/2024/v17i5440

Abstract

The main reason for this investigation is to use manual process of identification of time series components with two types of automated decomposition for time series known as automated BFTSC (break for time series components) and, automated GFTSC (Group for time series components) in detecting change in a volatile curve united states stock market (US SM). In identification of components of time series present in the seasonal data of US stock market. The data US Stock Market was a monthly data from January 2001 until December 2018 and a total of 18 years. The stock market of US is also available as a secondary data at the DataBank of University 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 were not captured by BFAST technique and it was included in the methodology of this study. BFTSC and GFTSC were considered to provide  a combined image of all the four components of time series while GFTSC had additional advantage of providing equations to the components automated. Evaluation using simulation data and empirical data vindicated the accuracy of  BFTSC and GFTSC based on linear trend less volatile data.  They 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 % of the entire components in the time series data in a linear trend data.

United State break for time series components seasonal data stock market cyclical components irregular components

References (25)

  1. 1 Google Earth Engine: Planetary-scale geospatial analysis for everyone [DOI]
  2. 2 Statistical pattern recognition: a review [DOI]
  3. 3 Time Series Analysis: Forecasting and Control [DOI]
  4. 4 Computation and analysis of multiple structural change models [DOI]
  5. 5 Trend changes in global greening and browning: contribution of short‐term trends to longer‐term change [DOI]
  6. 6 Osimertinib or platinum–pemetrexed in EGFR T790M–positive lung cancer
  7. 7 The Sustainability of Budget Deficits in a Stochastic Economy [DOI]
  8. 8 Sense from sequence reads: methods for alignment and assembly [DOI]
  9. 9 Detecting changes in vegetation trends using time series segmentation [DOI]
  10. 10 Volatility transmission between gold and oil futures under structural breaks [DOI]
  11. 11 Decomposition of Seasonal Time Series: A Model for the Census X-11 Program [DOI]
  12. 12 Performance of Combined Double Seasonal Univariate Time Series Models for Forecasting Water Demand [DOI]
  13. 13 When and How to Adjust Beyond the Business Cycle? A Guide to Structural Fiscal Balances [DOI]
  14. 14 Kalman filter with outliers and missing observations [DOI]
  15. 15 Near real-time tropical forest disturbance monitoring using Landsat time series and local expert monitoring data [DOI]
  16. 16 Prediction Changes for Nonstationary Multi-Temporal Satellite Images using HMM [DOI]
  17. 17 Monitoring domestic activities with scheduling techniques
  18. 18 Comparative study of Manual time series components identification with automated Break for Time Series Components (BFTSC) and Group for Time Series Components (GFTSC) in Identification in Univariate Forecasting
  19. 19 Group for Time Series Components (GFTSC) Identification of Gross Domestic Product (GDP) of United Kingdom (UK) [DOI]
  20. 20 Forecasting Australia Gross Domestic Product (GDP) under Structural Change (SC) Using Break for Time Series Components (BFTSC) [DOI]
  21. 21 Examining the Efficacy of Break for Time Series Components (BFTSC) and Group for Time Series Components (GFTSC) with Volatile Simulated and Empirical Data [DOI]
  22. 22 Prediction Changes for Nonstationary Multi-Temporal Satellite Images Using HMM [DOI]
  23. 23 International review of economics & finance
  24. 24 Asian Journal of Probability and Statistics [DOI]
  25. 25 BisPin and BFAST-Gap: Mapping Bisulfite-Treated Reads [DOI]

Cited by 2

Automated Detection Trend Breaks in the Identification of Ireland's Gross Domestic Product (GDP) for Accurate Forecasting

O. T. Olorunpomi, Ajare Emmanuel Oloruntoba, Hafsat Olaide Salah · International Journal of Computational and Applied Mathematics & Computer Science · 2026

Showing 1 of 2 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

6

Page views

0

PDF downloads

0

Outbound clicks

2

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.