Genetic algorithms for the identification of additive and innovation outliers in time series

Authors:
Roberto Baragona;Francesco Battaglia;Claudio Calzini
Affiliations:
Dipartimento di Sociologia, Universití di Roma "La Sapienza", Via Salaria 113, 00198 Roma, Italy;Dipartimento di Statistica Probabilití e Statistiche Applicate, Universití di Roma "La Sapienza", Piazzale Aldo Moro 5, 00185 Roma, Italy;Dipartimento di Sociologia, Universití di Roma "La Sapienza", Via Salaria 113, 00198 Roma, Italy
Venue:
Computational Statistics & Data Analysis
Year:
2001

Citing 3
Cited 3

Estimation of time series parameters in the presence of outliers

Technometrics
Genetic algorithms and their statistical applications: an introduction

Computational Statistics & Data Analysis
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

Classification tree analysis using TARGET

Computational Statistics & Data Analysis
Regression model selection using genetic algorithms

NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Statistics and Computing

Quantified Score

Hi-index	0.03

Visualization

Abstract

The detection of multiple outliers in time series is a cumbersome task because of the large number of combinations of the candidate locations. A genetic algorithm is proposed for the identification of additive and innovation outliers. The objective function depends on both the likelihood function and the number of outliers. Some case studies show that the algorithm is effective in detecting outliers' location and type and in estimating their size.