@article{ref1,
title="A new missing data imputation algorithm applied to electrical data loggers",
journal="Sensors (Basel)",
year="2015",
author="Crespo Turrado, Concepción and Sánchez Lasheras, Fernando and Calvo-Rollé, José Luis and Piñón-Pazos, Andrés José and de Cos Juez, Francisco Javier",
volume="15",
number="12",
pages="31069-31082",
abstract="Nowadays, data collection is a key process in the study of electrical power networks when searching for harmonics and a lack of balance among phases. In this context, the lack of data of any of the main electrical variables (phase-to-neutral voltage, phase-to-phase voltage, and current in each phase and power factor) adversely affects any time series study performed. When this occurs, a data imputation process must be accomplished in order to substitute the data that is missing for estimated values. This paper presents a novel missing data imputation method based on multivariate adaptive regression splines (MARS) and compares it with the well-known technique called multivariate imputation by chained equations (MICE). The results obtained demonstrate how the proposed method outperforms the MICE algorithm.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s151229842",
url="http://dx.doi.org/10.3390/s151229842"
}