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Residual XGBoost regression—Based individual moving range control chart for Gross Domestic Product growth monitoring

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Publicado en:PLoS One vol. 20, no. 5 (May 2025), p. e0321660
Autor principal: Aisy, Rahida Rihhadatul
Otros Autores: Zulfa, Latifatuz, Rahim, Yolanda, Ahsan, Muhammad
Publicado:
Public Library of Science
Materias:
Group of Twenty
Thailand
Brunei
Malaysia
Indonesia
Singapore
Accuracy
Risk management
Datasets
Macroeconomics
Forecasting
Trends
Global economy
Optimization techniques
Control charts
Statistical process control
Economic forecasting
Monitoring
Machine learning
Time series
Autocorrelation
Statistical analysis
Economic growth
Water quality
Economics
Process control
Neural networks
Support vector machines
Algorithms
Gross Domestic Product > GDP
Methods
Regulation of financial institutions
Economic
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:1932-6203
DOI:10.1371/journal.pone.0321660
Fuente:Health & Medical Collection