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DOI: 10.14569/IJACSA.2019.0101154
PDF

Improving Long Short-Term Memory Predictions with Local Average of Nearest Neighbors

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

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 11, 2019.

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Abstract: The study presented in this paper aims to improve the accuracy of meteorological time series predictions made with the recurrent neural network known as Long Short-Term Memory (LSTM). To reach this, instead of just making adjustments to the architecture of LSTM as seen in different related works, it is proposed to adjust the LSTM results using the univariate time series imputation algorithm known as Local Average of Nearest Neighbors (LANN) and LANNc which is a variation of LANN, that allows to avoid the bias towards the left of the synthetic data generated by LANN. The results obtained show that both LANN and LANNc allow to improve the accuracy of the predictions generated by LSTM, with LANN being superior to LANNc. Likewise, on average the best LANN and LANNc configurations make it possible to outperform the predictions reached by another recurrent neural network known as Gated Recurrent Unit (GRU).

Keywords: Long Short-Term Memory; Local Average of Nearest Neighbors; univariate time series prediction; LANN; LANNc; Gated Recurrent Unit; GRU

Anibal Flores, Hugo Tito and Deymor Centty, “Improving Long Short-Term Memory Predictions with Local Average of Nearest Neighbors” International Journal of Advanced Computer Science and Applications(IJACSA), 10(11), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101154

@article{Flores2019,
title = {Improving Long Short-Term Memory Predictions with Local Average of Nearest Neighbors},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101154},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101154},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {11},
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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