Método de combinación de pronósticos usando modelos Bayesianos y una metaheurística, caso de estudio

Translated title of the contribution: Combination forecasting method using Bayesian models and a metaheuristic, case study

David Higuita-Alzate, Marisol Valencia-Cárdenas, Juan Carlos Correa-Morales

Research output: Contribution to journalArticlepeer-review

Abstract

Planning of demand forecasting for perishable products is important for any type of industry that manufactures or distributes, especially if it has a seasonal behavior and a difficult to predict variability. This paper proposes a metaheuristic based on Ant Colony Optimization (ACO) for the combination of forecasts of multiple products, based on three models: Mixed Linear Model (MLM), Bayesian Regression Model with Innovation (BRM) and Dynamic Linear Bayesian Model (BDLM), which are part of the proposed combination whose process is based on minimizing the Mean of Absolute percentage Error (SMAPE) indicator. It is found that the BDLM and BRM methodologies obtain good results on an individual basis, being better BRM, however, the ACO algorithm designed yields a better result, facilitating an adequate prediction of the demand of several products of a company in the meat buffer sector.

Translated title of the contributionCombination forecasting method using Bayesian models and a metaheuristic, case study
Original languageSpanish
Pages (from-to)337-345
Number of pages9
JournalDYNA (Colombia)
Volume85
Issue number207
DOIs
Publication statusPublished - Oct 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© The author; licensee Universidad Nacional de Colombia.

All Science Journal Classification (ASJC) codes

  • General Engineering

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