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Research Article | Open Access |

Model for Time Series Imputation based on Average of Historical Vectors, Fitting and Smoothing

Author 1: Anibal Flores Author 2: Hugo Tito Author 3: Deymor Centty
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 10, No. 10 · Published 2019 · Cited by 7

DOI: https://doi.org/10.14569/IJACSA.2019.0101049

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Abstract

This paper presents a novel model for univariate time series imputation of meteorological data based on three algorithms: The first of them AHV (Average of Historical Vectors) estimates the set of NA values from historical vectors classified by seasonality; the second iNN (Interpolation to Nearest Neighbors) adjusts the curve predicted by AHV in such a way that it adequately fits to the prior and next value of the NAs gap; The third LANNf allows smoothing the curve interpolated by iNN in such a way that the accuracy of the predicted data can be improved. The results achieved by the model are very good, surpassing in several cases different algorithms with which it was compared.

Keywords

Univariate time series imputation average of historical vectors interpolation to nearest neighbors

How to Cite this Article

Flores, A., Tito, H., & Centty, D. (2019). Model for Time Series Imputation based on Average of Historical Vectors, Fitting and Smoothing. International Journal of Advanced Computer Science and Applications, 10(10). https://doi.org/10.14569/IJACSA.2019.0101049

Flores, Anibal, et al.. "Model for Time Series Imputation based on Average of Historical Vectors, Fitting and Smoothing." International Journal of Advanced Computer Science and Applications, vol. 10, no. 10, 2019, https://doi.org/10.14569/IJACSA.2019.0101049.

@article{Flores2019,
  title     = {Model for Time Series Imputation based on Average of Historical Vectors, Fitting and Smoothing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {10},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Anibal Flores and Hugo Tito and Deymor Centty},
  doi       = {10.14569/IJACSA.2019.0101049},
  url       = {https://doi.org/10.14569/IJACSA.2019.0101049}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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