Title: Bayesian forecasting of demand time-series data with zero values

Authors: Ana Corberán-Vallet; José D. Bermúdez; Enriqueta Vercher

Addresses: Department of Statistics and Operations Research, University of Valencia, Dr. Moliner 50, E-46100 Burjassot, Spain ' Department of Statistics and Operations Research, University of Valencia, Dr. Moliner 50, E-46100 Burjassot, Spain ' Department of Statistics and Operations Research, University of Valencia, Dr. Moliner 50, E-46100 Burjassot, Spain

Abstract: This paper describes the development of a Bayesian procedure to analyse and forecast positive demand time-series data with a proportion of zero values and a high level of variability for the non-zero data. The resulting forecasts play decisive roles in organisational planning, budgeting, and performance monitoring. Exponential smoothing methods are widely used as forecasting techniques in industry and business. However, they can be unsuitable for the analysis of non-negative demand time-series data with the aforementioned features. In this paper, an unconstrained latent demand underlying the observed demand is introduced into the linear heteroscedastic model associated with the Holt-Winters model. Accurate forecasts for the observed demand can readily be derived from those obtained with exponential smoothing for the latent demand. The performance of the proposed procedure is illustrated using a simulation study and two real time-series datasets which correspond to tourism demand and book sales. [Received 4 November 2010; Revised 7 September 2011, 10 April 2012; Accepted 10 May 2012]

Keywords: Bayesian forecasting; exponential smoothing; zero demand points; Holt-Winters model; censored data; supply chain planning; tourism demand; book sales; Spain; supply chain management; SCM; modelling.

DOI: 10.1504/EJIE.2013.058394

European Journal of Industrial Engineering, 2013 Vol.7 No.6, pp.777 - 796

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 22 Dec 2013 *

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