Modeling Time Series with Missing and Incorrect Values Using Self Adaptive Genetic Algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work it is presented a methodological proposal to build models for Time Series with missing and erroneous values. This methodology consist of two stages: first, it is realized an estimating of the missing and erroneous values of the series; and second, it is built a model for the series. The proposal is based on Self Adaptive Genetic Algorithms that were especially utilized to calculate ARMA models for the NN5-REDUCED problems which results are presented in this work. This methodology here presented can be generalized for the treatment of this type of Time Series by other non linear models that use, for example, neuronal networks, fuzzy logic, etc.
Translated title of the contributionConferencia Internacional en Inteligencia Artificial. Genética y Métidos Evolucionarios (GEM): Modelo de Series de Tiempo para detección de valores incorrectos utilizando Algoritmos Genéticos.
Original languageAmerican English
Title of host publicationInternational Conference Inteligence Artificial. General Topics in Artificial Intelligence. Genetic and Evolutionary Methods (GEM)
Subtitle of host publication Modeling Time Series with Missing and Incorrect Values Using Self Adaptive Genetic Algorithms
Place of PublicationEstados Unidos de Norteamerica
Chapter1
Pages175-180
Number of pages6
Volume1
Edition2011
StatePublished - 15 Jul 2011

Cite this

Cota Ortiz, M. D. G., & Flores Perez, P. (2011). Modeling Time Series with Missing and Incorrect Values Using Self Adaptive Genetic Algorithms. In International Conference Inteligence Artificial. General Topics in Artificial Intelligence. Genetic and Evolutionary Methods (GEM): Modeling Time Series with Missing and Incorrect Values Using Self Adaptive Genetic Algorithms (2011 ed., Vol. 1, pp. 175-180). [3].