Model for Time Series Imputation based on Average of Historical Vectors, Fitting and Smoothing
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 10
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.
Authors and Affiliations
Anibal Flores, Hugo Tito, Deymor Centty
Keywords
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- EP ID EP665120
- DOI 10.14569/IJACSA.2019.0101049
- Views 98
- Downloads 0
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