A Comparison of a Radial Basis Function Neural Network with other Methods for Estimating Missing Values in Univariate Time Series
DOI:
https://doi.org/10.33095/jeas.v28i134.2426Keywords:
univariate time series, Radial Basis Function, Bi-directional Recurrent Neural Networks, Adaptive response rate exponential smoothing, Next observation carried backward.Abstract
Missing values in the time-series data set have an impact on the correct decision-making in the future. Since complete data helps to obtain high accuracy in the estimation process, the reason for missing values is a malfunction of the measuring device or an error in the data entry process by the person. The research aims to compare the radial basis function methods with other methods to estimate missing values in univariate time series data. the simulation method was used to compare the methods to estimate the missing values, and that was used the Box-Jenkins model AR(1) once with the value of and once with the value of and with different sample sizes (60,100,300), assuming four percentages of missing from data values are missing at random MAR (5%,10%,15%,20%). The accuracy of the estimation of the methods was evaluated by using the standard of accuracy, the mean sum of squares error (MSE). from the results obtained using simulation, it was found that the(RBF) method is the best method for estimating the missing values in both values, in all sizes, and all loss ratios because it produces the lowest value of the average square error compared to other methods.
Paper type: Research paper
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