Addressing missing values is important in the process of getting a precise and accurate result. If missing data are not treated appropriately, then the results could lead to biased estimates. But different series may require different strategies to estimate these missing values. Seasonal data has a repetitive cycle that is predictable. By disaggregating the data into it seasonal factors, clear information behavior of the data could be observed and will make it easier to deal with the missing value. In this paper, the performance of three different methods is being compared with each other. One of the imputation methods will used information from the seasonality for the missing values to enhance the imputation technique. the other two methods are mean interpolation and AR model as the missing values imputation. Wind speed data from Alor Setar, Malaysia are used for this purpose. From the error measurement, the enhanced technique gives the best performance compared to the other two techniques.
