## 3 Results  
After analyzing in detail the rainfall data from the different stations of the network, in Step 1, 62 target stations that did not have large blocks of missing data were chosen to apply the proposed validation method between the years 2007 and 2018, which were both inclusive. Taking the Marco da Curra station as an example of a target station, in Figure 9 it can be seen that the total percentage of missing data was only 1.04 % and also that the percentages of missing data per month were low except in December 2011 with 22.58 %, in August 2014 with 19.20 %, and in April 2011 with 18.03 %. In Step 2, nine candidate stations were chosen by proximity for each target station. In Figure 10, you can see the selection of the candidates for the Marco da Curra station. Figure 10: The target station in yellow and candidate stations for Marco da Curra. Figure 9: Missing data ratio in Marco da Curra. Figure 11 shows the rainfall in March 2007 at the Marco da Curra target station. Two missing data blocks were observed in this station, with the first being much larger than the second, which corresponded to the 414 missing data items in that month. In Step 3, the experimental months were created. In March 2007, a pattern of losses that corresponded to the 414 missing data was saved (Figure 7a). It can also be verified that in the other years, there were no missing data items during this month, so this pattern of losses was applied to them to generate the experimental months. An example of this process for the month of March 2017 can be observed; Figure 12 shows the rainfall at the target station, and it can be verified that there were no missing data. In Figure 13, we see superimposed in red the loss pattern of 2007 applied to 2017, and the final result we obtained can be seen in Figure 14. For the month of March in this station, a total of 11 experimental data sets were created from _exp.mar.2008.p2007_ to _exp.mar.2018.p2007_. For January 3, patterns were saved for the years 2009, 2010, and 2017; next, they were applied to the other 9 years, so 27 experimental months were created for this month. In summary, a total of 218 experimental months were created for the Marco da Curra station. Once the process was finished for all the selected target stations, a total of 7555 experimental months were created. Among the candidate stations, those stations that have complete data for that month must be chosen as predictors. Figure 15 shows the rainfall data for the nine candidate stations. It can be seen that there were no missing data, and the time series data were very similar, although in the CIS Ferrol station, the amount of rainfall was greater than in other stations. In the lower part of Figure 15, the experimental month for the Marco da Curra target station is shown in blue with the missing data that we have artificially created. The next criterion applied is to choose from among the candidate stations those that are well correlated with the target station, thereby discarding the others. In Figure 16, it can be seen that it was possible to improve this correlation by shifting the series before imputing. This is logical because the rain moves from east to west with the fronts, and, depending on the geographical situation of each station, this rain can occur a few minutes before or after, which is why the maximum correlation between the time series is sometimes achieved by advancing a series and other times by delaying it. Once the candidate selection process is finished, the data set will consist of a matrix with only the target station and the predictor stations data in the columns, which are then ready to compute the different imputation algorithms. After the imputation with the four methods, the precision of all the time series data reconstructed with the complete original series was verified by means of the MAE, RE, RMSE, and NRMSE errors. The results obtained are shown in Figure 17, and it can be verified that missForest is the method that offered better precision.