Improving Gated Recurrent Unit Predictions with Univariate Time Series Imputation Techniques

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.

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