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On Selecting a Power Transformation in Time‐Series Analysis
Author(s) -
Chen Cathy W. S.,
Lee Jack C.
Publication year - 1997
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199709)16:5<343::aid-for665>3.0.co;2-j
Subject(s) - markov chain monte carlo , gibbs sampling , series (stratigraphy) , transformation (genetics) , computer science , bayesian probability , markov chain , time series , monte carlo method , econometrics , algorithm , statistics , data mining , mathematics , artificial intelligence , machine learning , paleontology , biochemistry , chemistry , gene , biology
The primary aim of this paper is to select an appropriate power transformation when we use ARMA models for a given time series. We propose a Bayesian procedure for estimating the power transformation as well as other parameters in time series models. The posterior distributions of interest are obtained utilizing the Gibbs sampler, a Markov Chain Monte Carlo (MCMC) method. The proposed methodology is illustrated with two real data sets. The performance of the proposed procedure is compared with other competing procedures. © 1997 John Wiley & Sons, Ltd.