Centro de Cálculo, Faculty of Engineering (11)
CC 30, Montevideo, Uruguay
The outlier detection phase is presented in a companion paper, and here the different techniques applied in order to imputate the missing values are described. The comparative results obtained with an hourly dataset of 15 years long are also presented. Two different problems have been simulated numerically: systematic missing values (i.e. at fixed hours) and non systematic ones.
Five different criteria were applied: imputation with the historical mean value; linear time interpolation within single station records; optimum interpolation (kriging) and the two newly developed Penalty Of the Principal Scores and linear Time Interpolation of the Principal Scores which considers all station records in a multivariate fashion; they prove to be the most accurate for this particular wind dataset. There is also some evidence of oversampling in time.