Missing Data Imputation in Time Series by Evolutionary Algorithms

Abstract

This paper presents a proposal based in an Evolutionary algorithm for imputing missing observations in Time Series. A genetic algorithm based on the minimization of an error function derived from their autocorrelation function, mean and variance, is presented. All methodological aspects of the genetic structure are presented. An extended explanation of the design of the Fitness Function is provided. Four application examples are provided and solved by the proposed method.