## 4 Discussion and Conclusions  
The objective of this study is to propose a method that allows us to compare the results obtained using different methods of imputation for missing values. Unlike other studies that have worked with daily, monthly, or annual data calculated from the ten-minute data provided by the rain sensors of the meteorological stations, in the present work, ten-minute data from 62 meteorological stations of Galicia were used, thereby seeking greater precision in the results. This implies greater complexity when making imputations because very large data sets have to be handled, and computing times are high.  
In this study, the imputation methods were validated with existing data; these experimental data sets were created using real loss patterns due to the mechanisms that caused them to be unknown.  
Table 2 summarizes the model validation statistics in terms of the MAE, RE, RMSE, NRMSE, BIAS, \(R^{2}\), and D for reconstructed precipitation based on ten-minute values in Galicia. These parameters were calculated station by station and allowed for comparison of the simulated time series with the real series. The data of the original series are public and can be downloaded from [[https://www.meteogalicia.gal](https://www.meteogalicia.gal)]([https://www.meteogalicia.gal](https://www.meteogalicia.gal)) (accessed on 1 November 2023) to reproduce the study.  
According to the results obtained, missForest presented the lowest error percentages and also introduced a similar bias as Amelia II, which were smaller than the other two imputation methods. Those errors are quite satisfactory in statistical terms.  
Regarding the similarity of the reconstructed time series with the original series, missForest also yielded better results than the other three methods. The means, variances, and quantiles can be also calculated station by station to determine the uncertainty of the imputation methods.  
This methodology lets researchers incorporate new imputation methods in Step 3.c.v and compare them with the obtained results. What is more, the next step that will be taken in the future is to compare the execution of this algorithm with different parameters to determine if other values can improve the obtained results. This will influence the time  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **MAE** & **RE** & **RMSE** & **NRMSE** & **BIAS** & \(\mathbf{R^{2}}\) & **D** & \\ \hline \multirow{6}{*}{missForest} & 0.015 & 2.606 & 0.065 & 0.649 & \(-\)0.006 & 0.119 & 0.476 & Min \\  & 0.024 & 3.333 & 0.095 & 0.763  
execution, which also has to be studied, and if it is very slow, the execution will be made on a cluster of computers to reduce this time.  
To study the influence of geographical circumstances on the results of the imputations, we want to classify stations by height and/or coordinates and compare the obtained results. Given the seasonal influence on the rainfall variable, a comparison will also be made of the results of the imputations carried out in spring, summer, autumn and winter. It is even being considered to combine this study with the previous one, given that seasonality in Galicia grows from north to south, as was previously mentioned.  
This research received no external funding.  
This data can be found here: [[https://www.meteogalicia.gal/observacion/rede/redeIndex.action?request_locale=gl](https://www.meteogalicia.gal/observacion/rede/redeIndex.action?request_locale=gl)]([https://www.meteogalicia.gal/observacion/rede/redeIndex.action?request_locale=gl](https://www.meteogalicia.gal/observacion/rede/redeIndex.action?request_locale=gl)).  
The authors declare no conflict of interest.