Time Series Imputation Using Genetic Programming and Lagrange Interpolation

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Damares C. O. {de Resende} and 
                 Adamo Lima {de Santana} and Fabio Manoel Franca Lobato",
  booktitle =    "2016 5th Brazilian Conference on Intelligent Systems
  title =        "Time Series Imputation Using Genetic Programming and
                 Lagrange Interpolation",
  year =         "2016",
  pages =        "169--174",
  abstract =     "Time series have been used in several applications
                 such as process control, environment monitoring,
                 financial analysis and scientific researches. However,
                 in the presence of missing data, this study may become
                 more complex due to a strong break of correlation among
                 samples. Therefore, this work proposes an imputation
                 method for time series using Genetic Programming (GP)
                 and Lagrange Interpolation. The heuristic adopted
                 builds an interpretable regression model that explores
                 time series statistical features such as mean, variance
                 and auto-correlation. It also makes use of
                 interrelation among multivariate time series to
                 estimate missing values. Results show that the proposed
                 method is promising, being capable of imputing data
                 without loosing the dataset's statistical properties,
                 as well as allowing a better understanding of the
                 missing data pattern from the obtained interpretable
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/BRACIS.2016.040",
  month =        oct,
  notes =        "Also known as \cite{7839581}",

Genetic Programming entries for Damares C O de Resende Adamo Lima de Santana Fabio Manoel Franca Lobato