What question did this study set out to answer?

The research aims to evaluate the reliability of municipal infrastructure systems in Tanzania using time-series forecasting models.

March 8, 2026Open Access

Time-Series Forecasting Model Evaluation of Municipal Infrastructure Assets Systems in Tanzania,

Key Points

The research aims to evaluate the reliability of municipal infrastructure systems in Tanzania using time-series forecasting models.
Conducted a comparative analysis of time-series forecasting models across various municipalities in Tanzania.
Utilized the ARIMA model as the primary forecasting method to predict asset condition changes.
Employed multiple regression analysis for statistical evaluation of the infrastructure systems.
The ARIMA model demonstrated superior performance compared to other forecasting models.
Predictions of asset condition changes were made with a confidence interval of ±5% over a five-year period.
Findings emphasize the potential for improved municipal infrastructure management through effective forecasting.

Abstract

The study examines municipal infrastructure assets systems in Tanzania, focusing on their reliability over time. A comparative study using time-series forecasting models to assess system reliability across different municipalities in Tanzania. The evaluation incorporates multiple regression analysis as the primary statistical model. The findings indicate that the ARIMA (AutoRegressive Integrated Moving Average) model outperforms other models, predicting asset condition changes with a confidence interval of ±5% over a five-year horizon. This study provides insights into enhancing municipal infrastructure management in Tanzania through robust forecasting methodologies. Municipalities should implement the ARIMA model for regular asset assessments to improve system reliability and resource allocation efficiency. Tanzania, municipal infrastructure, time-series forecasting, ARIMA, reliability The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

Time-Series Forecasting Model Evaluation of Municipal Infrastructure Assets Systems in Tanzania,

Key Points

Abstract

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