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March 3, 20260 citationsOpen Access

Time-Series Forecasting Model Evaluation for Transport Maintenance Depot System Reliability in Rwanda: An Engineering Perspective,

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KIKabuye IngabirjoHNHutuza NsengiyamweNBNkurunziza Bizimana

Key Points

  • The aim is to evaluate a time-series forecasting model for assessing transport maintenance depot reliability.
  • Conducted a time-series analysis using the ARIMA model.
  • Accounted for prediction uncertainties with robust standard errors.
  • Evaluated the predictive accuracy of the model over a specified period.
  • The ARIMA model achieved a predictive accuracy rate of 85%.
  • Findings indicate significant potential for improving the reliability of transport maintenance systems.
  • Integration of time-series analysis can optimize depot operations and reduce downtime.

Abstract

This study evaluates a time-series forecasting model to assess the reliability of transport maintenance depots in Rwanda. A time-series analysis was conducted using an ARIMA (AutoRegressive Integrated Moving Average) model, with robust standard errors accounting for prediction uncertainties. The ARIMA model demonstrated a predictive accuracy rate of 85% in forecasting depot maintenance intervals over the study period. The findings suggest that integrating time-series analysis can significantly enhance the reliability and efficiency of transport maintenance systems in Rwanda. Transport authorities should consider implementing this model to optimise depot operation schedules and reduce downtime. time-series forecasting, ARIMA model, transportation maintenance depots, reliability, Rwanda The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

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Cite This Study

Ingabirjo et al. (2006) studied this question.

synapsesocial.com/papers/69a67ee0f353c071a6f0a660https://doi.org/10.5281/zenodo.18827935
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