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

Baragona, Roberto; Battaglia, Francesco; Calzini, Claudio

doi:10.1016/s0167-9473(00)00058-x

Cited by 23 publications

(11 citation statements)

References 27 publications

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“…In statistics, AESAMC can find a variety of applications, such as model selection (Ferri and Piccioni 1992;Winker 1995;Wu and Chang 2002), experimental design (Angelis et al 2001), parameter estimation (Alcock and Burrage 2004), curve fitting (Baragona et al 2001), and clustering (Duczmal and Assuncão 2004;Duczmal et al 2007).…”

Section: Discussionmentioning

confidence: 99%

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Liang

2010

Stat Comput

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In this paper, we propose a new algorithm, the so-called annealing evolutionary stochastic approximation Monte Carlo (AESAMC) algorithm as a general optimization technique, and study its convergence. AESAMC possesses a self-adjusting mechanism, whose target distribution can be adapted at each iteration according to the current samples. Thus, AESAMC falls into the class of adaptive Monte Carlo methods. This mechanism also makes AE-SAMC less trapped by local energy minima than nonadaptive MCMC algorithms. Under mild conditions, we show that AESAMC can converge weakly toward a neighboring set of global minima in the space of energy. AESAMC is tested on multiple optimization problems. The numerical results indicate that AESAMC can potentially outperform simulated annealing, the genetic algorithm, annealing stochastic approximation Monte Carlo, and some other metaheuristics in function optimization.

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Section: Discussionmentioning

confidence: 99%

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Liang

2010

Stat Comput

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“…Each new individual of generation t þ 1 has as its parents two individuals from the previous generation t. These are selected by first computing for each individual j in generation t a uniform random variable in the interval ½0; 1=BIC 0 j . 1 The two individuals with the largest values of this random variable are then selected as parents. In this way the best individuals, that is the ones with the smallest values of the fitness function BIC 0 , are more likely to pass their genes onto the next generation.…”

Section: A Genetic Algorithm For Outlier Detectionmentioning

confidence: 99%

“…Therefore, and also to find appropriate values of the termination criterion for each application, the algorithm was run several times, using different seeds for the random number generator each time. 1 Where BIC 0 j denotes the BIC 0 value of individual j. This interval can be used in this situation since the BIC 0 values are all larger than one.…”

Section: A Genetic Algorithm For Outlier Detectionmentioning

confidence: 99%

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Genetic algorithms for outlier detection and variable selection in linear regression models

Tolvi

2003

Soft Computing

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This article addresses some problems in outlier detection and variable selection in linear regression models. First, in outlier detection there are problems known as smearing and masking. Smearing means that one outlier makes another, non-outlier observation appear as an outlier, and masking that one outlier prevents another one from being detected. Detecting outliers one by one may therefore give misleading results. In this article a genetic algorithm is presented which considers different possible groupings of the data into outlier and non-outlier observations. In this way all outliers are detected at the same time. Second, it is known that outlier detection and variable selection can influence each other, and that different results may be obtained, depending on the order in which these two tasks are performed. It may therefore be useful to consider these tasks simultaneously, and a genetic algorithm for a simultaneous outlier detection and variable selection is suggested. Two real data sets are used to illustrate the algorithms, which are shown to work well. In addition, the scalability of the algorithms is considered with an experiment using generated data.

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“…Some of the authors include; Denby and Martin [15], Pena [33], Tsay [45], Chang, Tiao and Chan [11] in which they all use iterative procedure for the detection of outliers. R. Baragona, F. Battaglia and D. Cucina [5] "proposed Identification and estimation of outliers in time series by using empirical likelihood methods." Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types.…”

Section: Introductionmentioning

confidence: 99%

Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series

Oluyomi.¹

2016

AJTAS

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This work is focused on developing two outlier generating mechanisms for the detection of outliers in the multivariate time series setting that is capable of ameliorating the swamping effect on regular observations in time series data. Specifying two-variable Vector Autoregressive (VAR) models and assuming innovative and multiplicative effect of outliers on time series data, the magnitude and variance of outlier were derived for the generating models by method of least squares. A modified test statistics were also developed to detect single outliers both in the response and explanatory variables. Real and simulated data were used to establish the validity of the models. The results show that the multiplicative is better than the additive model in terms of the number of outliers detected and the residual variance. This result is in line with previous studies in outlier detection in univariate time series.

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

Cited by 23 publications

References 27 publications

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Genetic algorithms for outlier detection and variable selection in linear regression models

Performance of Two Generating Mechanisms in Detection of Outliers in Multivariate Time Series

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