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DOI: 10.14569/IJACSA.2019.0101290
PDF

Improving Gated Recurrent Unit Predictions with Univariate Time Series Imputation Techniques

Author 1: Anibal Flores
Author 2: Hugo Tito
Author 3: Deymor Centty

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 12, 2019.

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Abstract: The work presented in this paper has its main objective to improve the quality of the predictions made with the recurrent neural network known as Gated Recurrent Unit (GRU). For this, instead of making different adjustments to the architecture of the neural network in question, univariate time series imputation techniques such as Local Average of Nearest Neighbors (LANN) and Case Based Reasoning Imputation (CBRi) are used. It is experimented with different gap-sizes, from 1 to 11 consecutive NAs, resulting in the best gap-size of six consecutive NA values for LANN and for CBRi the gap-size of two NA values. The results show that both imputation techniques allow improving prediction quality of Gated Recurrent Unit, being LANN better than CBRi, thus the results of the best configurations of LANN and CBRi allowed to surpass the techniques with which they were compared.

Keywords: Gated recurrent unit; local average of nearest neighbors; case based reasoning imputation; GRU+LANN; GRU+CBRi

Anibal Flores, Hugo Tito and Deymor Centty, “Improving Gated Recurrent Unit Predictions with Univariate Time Series Imputation Techniques” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101290

@article{Flores2019,
title = {Improving Gated Recurrent Unit Predictions with Univariate Time Series Imputation Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101290},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101290},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Anibal Flores and Hugo Tito and Deymor Centty}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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