Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data

This study introduced the optimized block bootstrap (OBB), a novel method designed to enhance time series prediction by reducing the number of blocks while maintaining their representativeness. OBB minimized block overlap, resulting in greater computational efficiency while preserving the temporal s...

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Main Authors: James Daniel, Kayode Ayinde, Adewale F. Lukman, Olayan Albalawi, Jeza Allohibi, Abdulmajeed Atiah Alharbi
Format: Article
Language:English
Published: AIMS Press 2024-10-01
Series:AIMS Mathematics
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Online Access:https://www.aimspress.com/article/doi/10.3934/math.20241487?viewType=HTML
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author James Daniel
Kayode Ayinde
Adewale F. Lukman
Olayan Albalawi
Jeza Allohibi
Abdulmajeed Atiah Alharbi
author_facet James Daniel
Kayode Ayinde
Adewale F. Lukman
Olayan Albalawi
Jeza Allohibi
Abdulmajeed Atiah Alharbi
author_sort James Daniel
collection DOAJ
description This study introduced the optimized block bootstrap (OBB), a novel method designed to enhance time series prediction by reducing the number of blocks while maintaining their representativeness. OBB minimized block overlap, resulting in greater computational efficiency while preserving the temporal structure of data. The method was evaluated through extensive simulations of autoregressive moving average (ARMA) models and South Africa economic data which included inflation rates, gross domestic product (GDP) growth, interest rates, and unemployment rates. Results demonstrated that OBB consistently outperformd circular block bootstrap (CBB), providing more accurate forecasts with lower root mean square error (RMSE), which assessed variance, and lower mean absolute error (MAE), which measured bias, across various time series models and parameter settings. Consequently, the OBB method was applied to forecasting of the South Africa economic data, extending up to 2027. The novel approach presented by OBB offered a valuable tool for improving predictive accuracy in time series forecasting, with potential applications across diverse fields such as finance and environmental modeling.
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issn 2473-6988
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spelling doaj-art-b0692de195964675835c640581c324ba2025-08-20T02:13:52ZengAIMS PressAIMS Mathematics2473-69882024-10-01911307813081510.3934/math.20241487Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series dataJames Daniel 0Kayode Ayinde 1Adewale F. Lukman2Olayan Albalawi3Jeza Allohibi4Abdulmajeed Atiah Alharbi 51. Department of Statistics, Federal University of Technology, Akure, Nigeria2. Northwest Missouri State University, Maryvile, USA3. Department of Mathematics and Statistics, University of North Dakota, Grand Forks, North Dakota, USA4. Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia5. Department of Mathematics, Faculty of Science, Taibah University, Al-Madinah Al-Munawara 42353, Saudi Arabia5. Department of Mathematics, Faculty of Science, Taibah University, Al-Madinah Al-Munawara 42353, Saudi ArabiaThis study introduced the optimized block bootstrap (OBB), a novel method designed to enhance time series prediction by reducing the number of blocks while maintaining their representativeness. OBB minimized block overlap, resulting in greater computational efficiency while preserving the temporal structure of data. The method was evaluated through extensive simulations of autoregressive moving average (ARMA) models and South Africa economic data which included inflation rates, gross domestic product (GDP) growth, interest rates, and unemployment rates. Results demonstrated that OBB consistently outperformd circular block bootstrap (CBB), providing more accurate forecasts with lower root mean square error (RMSE), which assessed variance, and lower mean absolute error (MAE), which measured bias, across various time series models and parameter settings. Consequently, the OBB method was applied to forecasting of the South Africa economic data, extending up to 2027. The novel approach presented by OBB offered a valuable tool for improving predictive accuracy in time series forecasting, with potential applications across diverse fields such as finance and environmental modeling.https://www.aimspress.com/article/doi/10.3934/math.20241487?viewType=HTMLbootstrap methodautoregressive moving average (arma)measures of accuracycircular block bootstrap (cbb)optimised block bootstrap (obb)
spellingShingle James Daniel
Kayode Ayinde
Adewale F. Lukman
Olayan Albalawi
Jeza Allohibi
Abdulmajeed Atiah Alharbi
Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data
AIMS Mathematics
bootstrap method
autoregressive moving average (arma)
measures of accuracy
circular block bootstrap (cbb)
optimised block bootstrap (obb)
title Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data
title_full Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data
title_fullStr Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data
title_full_unstemmed Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data
title_short Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data
title_sort optimised block bootstrap an efficient variant of circular block bootstrap method with application to south african economic time series data
topic bootstrap method
autoregressive moving average (arma)
measures of accuracy
circular block bootstrap (cbb)
optimised block bootstrap (obb)
url https://www.aimspress.com/article/doi/10.3934/math.20241487?viewType=HTML
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