Revisiting Time-Series Forecasts of Transport Maintenance Depot Systems in South Africa: A Methodological Validation Study
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2007
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| _version_ | 1866901037027164160 |
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| author | Mkhize, Nolwazi Ndlovu, Siyabonga |
| author_facet | Mkhize, Nolwazi Ndlovu, Siyabonga |
| contents | <p>This study revisits previous work on forecasting transport maintenance depot systems in South Africa to validate methodological approaches. The methodology involves re-analysis of existing data sets using advanced statistical tools such as ARIMA (AutoRegressive Integrated Moving Average) model equations to forecast future maintenance demands and identify trends. A key finding is that the application of robust standard errors significantly improves the accuracy of forecasts, reducing variance by approximately 15% compared to previous studies. The re-analysis confirms the effectiveness of time-series forecasting in predicting maintenance needs with a precision level indicated by the model's confidence interval. Further research should consider incorporating real-time data sources and integrating machine learning techniques for enhanced predictive accuracy. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18850053 |
| institution | Zenodo |
| language | eng |
| publishDate | 2007 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Revisiting Time-Series Forecasts of Transport Maintenance Depot Systems in South Africa: A Methodological Validation Study Mkhize, Nolwazi Ndlovu, Siyabonga Sub-Saharan African GPS Markov Simulation Regression Time-Series <p>This study revisits previous work on forecasting transport maintenance depot systems in South Africa to validate methodological approaches. The methodology involves re-analysis of existing data sets using advanced statistical tools such as ARIMA (AutoRegressive Integrated Moving Average) model equations to forecast future maintenance demands and identify trends. A key finding is that the application of robust standard errors significantly improves the accuracy of forecasts, reducing variance by approximately 15% compared to previous studies. The re-analysis confirms the effectiveness of time-series forecasting in predicting maintenance needs with a precision level indicated by the model's confidence interval. Further research should consider incorporating real-time data sources and integrating machine learning techniques for enhanced predictive accuracy. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.</p> |
| title | Revisiting Time-Series Forecasts of Transport Maintenance Depot Systems in South Africa: A Methodological Validation Study |
| topic | Sub-Saharan African GPS Markov Simulation Regression Time-Series |
| url | https://doi.org/10.5281/zenodo.18850053 |