Missing values remain a persistent challenge in time-series data, particularly within large-scale monitoring systems where reliable forecasting and evaluation are essential. Incomplete records often arise from irregular reporting, infrastructure limitations, or system failures, leading to biased analyses and inaccurate predictions. Traditional imputation methods, such as mean, median, and mode substitution, provide computational efficiency but oversimplify temporal structures. At the same time, more advanced approaches, including Multiple Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN), offer improvements yet remain sensitive to data distribution and model configuration. To address this gap, this study introduces Sherwood Duel Optimization (SDO). This socio-inspired framework reconceptualizes imputation as a deterministic duel-based optimization problem. In its fixed form, SDO generates multiple candidate imputations and selects the most robust replacement value using a composite multi-metric scoring mechanism that integrates forecasting accuracy and explanatory power. The framework was evaluated using multivariate educational time-series data and further validated across heterogeneous SDG-related domains, and compared against classical and advanced baselines across three forecasting models. Experimental results demonstrate that SDO consistently outperforms existing methods, reducing forecasting error (MAPE) by more than 40%, achieving the lowest RMSE, and producing R² values exceeding 0.95. Statistical testing confirms that these improvements are significant across experimental configurations. These findings highlight the potential of SDO as a reliable, interpretable, and computationally efficient optimization-based imputation framework. By strengthening data reliability at the reconstruction stage, SDO enhances the credibility of downstream forecasting and decision-making in institutional and sustainability-oriented monitoring systems.
Utama et al. (Sat,) studied this question.