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DOI: 10.14569/IJACSA.2020.0110360
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Recurrent Neural Networks for Meteorological Time Series Imputation

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

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 3, 2020.

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Abstract: The aim of the work presented in this paper is to analyze the effectiveness of recurrent neural networks in imputation processes of meteorological time series, for this six different models based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are implemented and it is experimented with hourly meteorological time series such as temperature, wind direction and wind velocity. The implemented models have architectures of 2, 3 and 4 sequential layers and their results are compared with each other, as well as with other imputation techniques for univariate time series mainly based on moving averages. The results show that for temperature time series on average the recurrent neural network achieve better results than the imputation techniques based on moving averages; in the case of wind direction time series, on average only one model based on RNN manages to exceed the models based on moving averages; and finally, for wind velocity time series on average, no RNN-based model manages to exceed the results achieved by moving averages-based models.

Keywords: Recurrent neural network; long short-term memory; gated recurrent unit; univariate time series imputation

Anibal Flores, Hugo Tito and Deymor Centty, “Recurrent Neural Networks for Meteorological Time Series Imputation” International Journal of Advanced Computer Science and Applications(IJACSA), 11(3), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110360

@article{Flores2020,
title = {Recurrent Neural Networks for Meteorological Time Series Imputation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110360},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110360},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {3},
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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