"background": "Municipal infrastructure asset systems in Rwanda face challenges in long-term financial sustainability and performance measurement. Existing asset management frameworks often lack robust, data-driven methodologies for forecasting operational yield, which is critical for capital planning and maintenance scheduling. ", "purpose and objectives": "This article presents a methodological evaluation of a novel time-series forecasting model designed to measure and project yield improvement within these asset systems. The primary objective is to detail the model's construction, calibration, and validation protocol. ", "methodology": "The methodology integrates an autoregressive integrated moving average with exogenous variables (ARIMAX) model, specified as Yt = \ + =1^{p\ Yt-i + =1^q\ -j + =1^r\ Xt, k + \, where Yt is the yield metric. Model parameters were estimated using maximum likelihood, with inference on coefficients based on robust standard errors to account for heteroskedasticity. A structured back-testing procedure on historical data was employed for validation. ", "findings": "The methodological evaluation demonstrates that the model provides a statistically significant improvement in forecast accuracy over a naive benchmark, with a mean absolute percentage error reduction of approximately 18% in out-of-sample tests. Parameter estimates for key maintenance expenditure variables were positive and significant at the 95% confidence level, indicating their material impact on yield trajectories. ", "conclusion": "The proposed ARIMAX-based methodology offers a rigorous, replicable framework for forecasting infrastructure yield, enhancing the evidence base for strategic asset management decisions. ", "recommendations": "It is recommended that municipal engineers and planners adopt similar stochastic forecasting techniques, incorporating regular model updating with new data and sensitivity analysis around key exogenous inputs. ", "key words": "asset management, infrastructure yield, time-series analysis, ARIMAX, forecasting, municipal engineering", "contribution statement": "This paper provides
Uwimana et al. (Sat,) studied this question.