Multiple imputation methods: a case study of daily gold price

Data imputation strategies are necessary to address the prevalent difficulty of missing values in data observation and recording operations. This work utilizes diverse imputation methods to forecast and complete absent values inside a financial time-series dataset, specifically the daily prices of gold. The predictive accuracy of imputed data is assessed in comparison to the original entire dataset to ensure its robustness. The imputation methods are validated using actual closing price data obtained from a daily gold price website. The examined approaches include mean imputation, k-nearest neighbor (KNN), hot deck, random forest, support vector machine (SVM), and spline imputation. Their performance is evaluated based on several metrics, including mean error (ME), mean absolute error (MAE), root mean square error (RMSE), mean percentage error (MPE), and mean absolute percentage error (MAPE). The results indicate that the KNN approach consistently performs better than other methods in terms of all accuracy measures. Nevertheless, the precision of all techniques decreases as the proportion of missing data rises. Therefore, the KNN approach is suggested because to its exceptional performance and dependability in imputation tasks.