A sequential hybrid forecasting system for demand prediction

Luis Aburto, Richard Weber

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

Demand prediction plays a crucial role in advanced systems for supply chain management. Having a reliable estimation for a product's future demand is the basis for the respective systems. Various forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivated the development of hybrid systems combining different techniques and their respective advantages. Based on a comparison of ARIMA models and neural networks we propose to combine these approaches to a sequential hybrid forecasting system. In our system the output from an ARIMA-type model is used as input for a neural network which tries to reproduce the original time series. The applications on time series representing daily product sales in a supermarket underline the excellent performance of the proposed system.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 5th International Conference, MLDM 2007, Proceedings
PublisherSpringer Verlag
Pages518-532
Number of pages15
ISBN (Print)9783540734987
DOIs
StatePublished - 2007
Event5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007 - Leipzig, Germany
Duration: 18 Jul 200720 Jul 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4571 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007
Country/TerritoryGermany
CityLeipzig
Period18/07/0720/07/07

Keywords

  • ARIMA
  • Demand forecasting
  • Hybrid forecasts
  • Neural networks

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