Analysis and generalisation of a multivariate exponential smoothing model
Management Science
Applied multivariate analysis
A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
A decision support system methodology for forecasting of time series based on soft computing
Computational Statistics & Data Analysis
An integrated forecasting approach to hotel demand
Mathematical and Computer Modelling: An International Journal
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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.