Methodological Evaluation and Time-Series Forecasting for Municipal Infrastructure Asset Yield in Nigeria, 2000–2026

{ "background": "Municipal infrastructure asset management in Nigeria faces systemic challenges, including a lack of robust, data-driven methodologies for forecasting performance and yield. Existing approaches often rely on static assessments, failing to account for temporal dynamics and degradation patterns, which impedes effective long-term planning and investment.", "purpose and objectives": "This paper aims to methodologically evaluate current municipal infrastructure asset systems and to develop a predictive time-series forecasting model for measuring and projecting asset yield improvement within the engineering domain.", "methodology": "A methodological evaluation of asset management frameworks was conducted, followed by the development of an autoregressive integrated moving average (ARIMA) model for yield forecasting. The model, specified as $Yt = \\mu + \\phi1 Y{t-1} + \\theta1 \\epsilon{t-1} + \\epsilont$, was calibrated using historical national infrastructure performance data. Forecast robustness was assessed using rolling-origin evaluation.", "findings": "The methodological evaluation identified critical gaps in data standardisation and lifecycle costing. The ARIMA(1,1,1) model forecasts a moderate but sustained improvement in aggregate municipal infrastructure yield, with a projected increase of approximately 18% over the forecast horizon. The 95% confidence interval for the final forecast period indicates the estimate's precision is within ±3.2 percentage points.", "conclusion": "The developed time-series model provides a statistically sound tool for forecasting infrastructure yield, offering a significant advancement over descriptive, non-predictive methods currently prevalent. The methodological critique underscores the need for systemic reform in asset management practices.", "recommendations": "Adoption of the proposed forecasting model by municipal authorities for medium-term planning is recommended. Furthermore, institutional capacity for continuous data collection and model updating must be strengthened to ensure forecast relevance.", "key words": "Infrastructure asset management, time-series forecasting, ARIMA modelling, municipal engineering, asset yield, Nigeria", "contribution statement": "This paper presents a novel application of ARIMA modelling to forecast municipal