Closing the Data Gap: A Comparative Study of Missing Value Imputation Algorithms in Time Series Datasets
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53552-9_7
Reference18 articles.
1. Little, R.J., Rubin, D.B.: Statistical Analysis with Missing Data. Wiley, Hoboken (2019)
2. Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., Tabona, O.: A survey on missing data in machine learning. J. Big Data. 8 (2021). https://doi.org/10.1186/s40537-021-00516-9
3. Ahn, H., Sun, K., Kim, K.P.: Comparison of missing data imputation methods in time series forecasting. Comput. Mater. Continua. 70, 767–779 (2021). https://doi.org/10.32604/cmc.2022.019369
4. Jamaludin, K.R., Muhamad, W.Z.A.W., Miskon, S.: A review of current publications trend on missing data imputation over three decades: direction and future research (2021). https://doi.org/10.21203/rs.3.rs-996596/v1
5. Hasan, M.K., Alam, M.A., Roy, S., Dutta, A., Jawad, M.T., Das, S.: Missing value imputation affects the performance of machine learning: a review and analysis of the literature (2010–2021) (2021). https://doi.org/10.1016/j.imu.2021.100799
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