## 3 Results and Discussion
The CB method emerged as the most robust regression technique, maintaining remarkably consistent MAE values across different locations and scenarios. An interesting anomaly was observed at the Arad monitoring station, where the Kalman filter unexpectedly outperformed machine learning methods, highlighting the nuanced nature of data imputation challenges. The research emphasizes that MAE represents only one dimension of imputation quality. Extreme values in error series and their variability, as illustrated in Figure 8, play crucial roles in comprehensively evaluating interpolation effectiveness. The standard deviation of error values provides additional critical insights into the reliability and precision of different imputation approaches. The analysis of extreme value characteristics revealed nuanced insights across different imputation methodologies. The RF and GB methods demonstrated remarkable consistency, exhibiting the lowest occurrence of extreme values across multiple sampling locations. The Kalman filter presented an intriguing performance profile. While maintaining moderate MAE metrics, its effectiveness substantially diminished when confronting extreme value scenarios, positioning it in a comparatively weaker performance category. Figure 6: Imputed river flow values with random forest, Kalman filter, ratio method and CatBooster for sample #512 with 20% gaps—Glodeni station, monthly data series. Contrary to expectations, the ratio method--ranking lowest in MAE assessments--displayed better performance when addressing extreme value distributions. A robust, positively correlated relationship exists between the percentage of missing data and the frequency of extreme value occurrences, as illustrated in Figure 7.  
Figure 8: \(Cont\). Figure 7: Imputation error metrics for all stations in case of different methods by gap percentage. Error variance analysis revealed minimal ranking differentials compared to MAE positioning. The Arad station presented a notable exception, where the CB method distinguished itself by exhibiting the smallest variance, thereby occupying the optimal position. To comprehensively assess method performance, we developed a new metric, the Error Index (EI), by combining the three previously discussed indicators in their normalized forms. The weights, expressing their relative importance were calculated using pairwise correlation values, with each indicator's correlation values summed as absolute values and subsequently normalized as shown in Equations (6)-(8). Complementing the EI, a Performance Index (PI) was defined as the inverse value of the EI (Equation (9)). \[w_{mae}=|r_{mae-extr}|+|r_{mae-sd}|\;\;w_{extr}=|r_{extr-mae}|+|r_{extr-sd}|\;\;w_{sd} \tag{6}\]  
\[\begin{array}{c}W_{MAE}=\frac{w_{mae}}{w_{mae}+w_{ext}+w_{sd}}\;\;W_{EXTR}= \frac{w_{actr}}{w_{mae}+w_{ext}+w_{sd}}\;\;W_{SD  
to highlight performance insights. The cyan-highlighted variant represents the most effectively performing scenario for a given regressor, while the overall optimal method is distinguished in green (Figure 9). A clear performance pattern emerged, demonstrating the CB method's superior capabilities across multiple scenarios. The calculated imputation performance index revealed that the PS and PDS variants often outperformed the P method which underscores the importance of context-specific approach selection in data imputation. For each sample, we calculated the autocorrelation and compared these values to those of the original series. This analysis allowed us to determine the percentage of samples in which the imputed series did not differ significantly (at a significance level of 0.05) from the temporal correlation of the original series, indicating that these characteristics were preserved. The results obtained align with those indicated by the performance index, with the CB method ranking first in terms of the proportion of cases where the temporal correlation of the reconstructed samples does not differ significantly from that of the original series.