This paper proposes new methods of estimating missing values in time series data while comparing them with existing methods. The new methods are based on the row, column and overall averages of time series data arrang...This paper proposes new methods of estimating missing values in time series data while comparing them with existing methods. The new methods are based on the row, column and overall averages of time series data arranged in a Buys-Ballot table with m rows and s columns. The methods assume that 1) only one value is missing at a time, 2) the trending curve may be linear, quadratic or exponential and 3) the decomposition method is either Additive or Multiplicative. The performances of the methods are assessed by comparing accuracy measures (MAE, MAPE and RMSE) computed from the deviations of estimates of the missing values from the actual values used in simulation. Results show that, under the stated assumptions, estimates from the new method based on full decomposition of a series is the best (in terms of the accuracy measures) when compared with other two new and the existing methods.展开更多
文摘This paper proposes new methods of estimating missing values in time series data while comparing them with existing methods. The new methods are based on the row, column and overall averages of time series data arranged in a Buys-Ballot table with m rows and s columns. The methods assume that 1) only one value is missing at a time, 2) the trending curve may be linear, quadratic or exponential and 3) the decomposition method is either Additive or Multiplicative. The performances of the methods are assessed by comparing accuracy measures (MAE, MAPE and RMSE) computed from the deviations of estimates of the missing values from the actual values used in simulation. Results show that, under the stated assumptions, estimates from the new method based on full decomposition of a series is the best (in terms of the accuracy measures) when compared with other two new and the existing methods.
