Abstract: Usually, time series data suffers from high percentage of missing values which is related to its nature and its collection process. This paper proposes a data imputation technique for imputing the missing values in time series data. The Fuzzy Gaussian membership function and the Fuzzy Triangular membership function are proposed in a data imputation algorithm in order to identify the best imputation for the missing values where the membership functions were used to calculate weights for the data values of the nearest neighbor’s before using them during imputation process. The evaluation results show that the proposed technique outperforms traditional data imputation techniques where the triangular fuzzy membership function has shown higher accuracy than the gaussian membership function during evaluation.

Malak El-Bakry, Farid Ali, Ayman El-Kilany and Sherif Mazen, “Fuzzy based Techniques for Handling Missing Values” International Journal of Advanced Computer Science and Applications(IJACSA), 12(3), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120306

@article{El-Bakry2021,
title = {Fuzzy based Techniques for Handling Missing Values},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120306},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120306},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {3},
author = {Malak El-Bakry and Farid Ali and Ayman El-Kilany and Sherif Mazen}
}