Multivariate exponential smoothing: A Bayesian forecast approach based on simulation

José D. Bermúdez, Ana Corberán-Vallet and Enriqueta Vercher

Mathematics and Computers in Simulation (MATCOM), 2009, vol. 79, issue 5, 1761-1769

Abstract: This paper deals with the prediction of time series with correlated errors at each time point using a Bayesian forecast approach based on the multivariate Holt–Winters model. Assuming that each of the univariate time series comes from the univariate Holt–Winters model, all of them sharing a common structure, the multivariate Holt–Winters model can be formulated as a traditional multivariate regression model. This formulation facilitates obtaining the posterior distribution of the model parameters, which is not analytically tractable: simulation is needed. An acceptance sampling procedure is used in order to obtain a sample from this posterior distribution. Using Monte Carlo integration the predictive distribution is then approached. The forecasting performance of this procedure is illustrated using the hotel occupancy time series data from three provinces in Spain.

Keywords: Bayesian forecasting; Monte Carlo methods; Multivariate time series; Holt–Winters model; Variate generation (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:79:y:2009:i:5:p:1761-1769

DOI: 10.1016/j.matcom.2008.09.004

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