Residual XGBoost regression—Based individual moving range control chart for Gross Domestic Product growth monitoring
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| Vydáno v: | PLoS One vol. 20, no. 5 (May 2025), p. e0321660 |
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| Hlavní autor: | |
| Další autoři: | , , |
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Public Library of Science
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| On-line přístup: | Citation/Abstract Full Text Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3202352942 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0321660 |2 doi | |
| 035 | |a 3202352942 | ||
| 045 | 2 | |b d20250501 |b d20250531 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Aisy, Rahida Rihhadatul | |
| 245 | 1 | |a Residual XGBoost regression—Based individual moving range control chart for Gross Domestic Product growth monitoring | |
| 260 | |b Public Library of Science |c May 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate and reliable Gross Domestic Product (GDP) forecasting is indispensable for informed economic policymaking and risk management. Autocorrelation, a prevalent characteristic of macroeconomic time series, poses significant challenges to traditional forecasting methodologies and statistical process control. This study introduces a novel approach to GDP forecasting and monitoring by integrating XGBoost regression, a robust machine learning algorithm, with Individual and Moving Range (I-MR) control charts. By effectively capturing complex nonlinear relationships and mitigating autocorrelation, the proposed model offers enhanced predictive accuracy compared to conventional methods. Empirical results demonstrate the model’s efficacy in phase I, aligning closely with actual GDP values. However, phase II analysis reveals discrepancies, suggesting the need for further model refinement and the potential incorporation of additional economic indicators to improve forecast precision. | |
| 610 | 4 | |a Group of Twenty | |
| 651 | 4 | |a Thailand | |
| 651 | 4 | |a Brunei | |
| 651 | 4 | |a Malaysia | |
| 651 | 4 | |a Indonesia | |
| 651 | 4 | |a Singapore | |
| 653 | |a Accuracy | ||
| 653 | |a Risk management | ||
| 653 | |a Datasets | ||
| 653 | |a Macroeconomics | ||
| 653 | |a Forecasting | ||
| 653 | |a Trends | ||
| 653 | |a Global economy | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Control charts | ||
| 653 | |a Statistical process control | ||
| 653 | |a Economic forecasting | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Time series | ||
| 653 | |a Autocorrelation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Economic growth | ||
| 653 | |a Water quality | ||
| 653 | |a Economics | ||
| 653 | |a Process control | ||
| 653 | |a Neural networks | ||
| 653 | |a Support vector machines | ||
| 653 | |a Algorithms | ||
| 653 | |a Gross Domestic Product--GDP | ||
| 653 | |a Methods | ||
| 653 | |a Regulation of financial institutions | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Zulfa, Latifatuz | |
| 700 | 1 | |a Rahim, Yolanda | |
| 700 | 1 | |a Ahsan, Muhammad | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 5 (May 2025), p. e0321660 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3202352942/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3202352942/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3202352942/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |