A comparison of imputation methods for the consecutive missing temperature data

연속적 결측이 존재하는 기온 자료에 대한 결측복원 기법의 비교

  • Received : 2016.03.23
  • Accepted : 2016.03.31
  • Published : 2016.04.30


Consecutive missing values are likely to occur in long climate data due to system error or defective equipment. Furthermore, it is difficult to impute missing values. However, these complicated problems can be overcame by imputing missing values with reference time series. Reference time series must be composed of similar time series to time series that include missing values. We performed a simulation to compare three missing imputation methods (the adjusted normal ratio method, the regression method and the IDW method) to complete the missing values of time series. A comparison of the three missing imputation methods for the daily mean temperatures at 14 climatological stations indicated that the IDW method was better thanx others at south seaside stations. We also found the regression method was better than others at most stations (except south seaside stations).


consecutive missing value;missing value imputation;adjusted normal ratio methods;regression method;IDW method


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Supported by : Korea Meteorological Administration