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.

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