The Identication of Outliers in ARMAX Models via Genetic Algorithm

Abstract

This paper proposes a procedure to identify additive and innovational outliers by genetic algorithm in autoregressive moving average with exogenous variable(ARMAX) time series models. We use some methods to delete the influence of input process in ARMAX model and then detect outliers in time series based on the previous work, which is an improvement and extension of the detection method on ARMA models. Empirical and simulation studies show that the proposed procedure is effective.