Evolutionary Computing in Statistical Data Analysis

Baragona, Roberto; Battaglia, Francesco

doi:10.1007/978-3-642-01085-9_12

Cited by 3 publications

(3 citation statements)

References 69 publications

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“…An alternative coding and its advantages are described in Baragona and Battaglia (2009). Our implementation of the GA starts with an initial population of chromosomes generated at random.…”

Section: Applying Ga To the Identification Problemmentioning

confidence: 99%

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Periodic autoregressive model identification using genetic algorithms

Ursu

Turkman

2012

Journal Time Series Analysis

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Periodic autoregressive (PAR) models extend the classical autoregressive models by allowing the parameters to vary with seasons. Selecting PAR time-series models can be computationally expensive, and the results are not always satisfactory. In this article, we propose a new automatic procedure to the model selection problem by using the genetic algorithm. The Bayesian information criterion is used as a tool to identify the order of the PAR model. The success of the proposed procedure is illustrated in a small simulation study, and an application with monthly data is presented.

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“…An alternative coding and its advantages are described in Baragona and Battaglia (2009). Our implementation of the GA starts with an initial population of chromosomes generated at random.…”

Section: Applying Ga To the Identification Problemmentioning

confidence: 99%

“…Statistical applications of GAs have been discussed by Chatterjee et al. (1996), Baragona and Battaglia (2009), amongst others. Various versions of the GA have been proposed and shown to be useful for similar problems in time‐series analysis, such as multi‐regime models based on thresholds for describing structural breaks (Davis et al.…”

Section: Introductionmentioning

confidence: 99%

Periodic autoregressive model identification using genetic algorithms

Ursu

Turkman

2012

Journal Time Series Analysis

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show abstract

“…In the Twelfth Chapter, Baragona and Battaglia [12] Illustrate how evolutionary computation techniques have influenced the statistical theory and practice concerned with multivariate data analysis, time series model building and optimization. Chapter deals with variable selection in linear regression models, non linear regression, time series model identification and estimation, detection of outlying observations in time series with respect to location and type identification, cluster analysis and grouping problems, including clusters of directional data and clusters of time series.…”

Section: Part-ii: Global Optimization Algorithms: Applicationsmentioning

confidence: 99%