Vol.70, No.1, 2022, pp.767-779, doi:10.32604/cmc.2022.019369
Comparison of Missing Data Imputation Methods in Time Series Forecasting
  • Hyun Ahn1, Kyunghee Sun2, Kwanghoon Pio Kim3,*
1 Division of Software Convergence, Hanshin University, Gyeonggi, 18101, Korea
2 Contents Convergence Software Research Institute, Kyonggi University, Gyeonggi, 16227, Korea
3 Division of AI Computer Science and Engineering, Kyonggi University, Gyeonggi, 16227, Korea
* Corresponding Author: Kwanghoon Pio Kim. Email:
Received 11 April 2021; Accepted 23 May 2021; Issue published 07 September 2021
Time series forecasting has become an important aspect of data analysis and has many real-world applications. However, undesirable missing values are often encountered, which may adversely affect many forecasting tasks. In this study, we evaluate and compare the effects of imputation methods for estimating missing values in a time series. Our approach does not include a simulation to generate pseudo-missing data, but instead perform imputation on actual missing data and measure the performance of the forecasting model created therefrom. In an experiment, therefore, several time series forecasting models are trained using different training datasets prepared using each imputation method. Subsequently, the performance of the imputation methods is evaluated by comparing the accuracy of the forecasting models. The results obtained from a total of four experimental cases show that the -nearest neighbor technique is the most effective in reconstructing missing data and contributes positively to time series forecasting compared with other imputation methods.
Missing data; imputation method; time series forecasting; LSTM
Cite This Article
Ahn, H., Sun, K., Kim, K. P. (2022). Comparison of Missing Data Imputation Methods in Time Series Forecasting. CMC-Computers, Materials & Continua, 70(1), 767–779.
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