A sequential hybrid forecasting system for demand prediction

Luis Aburto, Richard Weber

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

17 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojadaMachine Learning and Data Mining in Pattern Recognition - 5th International Conference, MLDM 2007, Proceedings
EditorialSpringer Verlag
Páginas518-532
Número de páginas15
ISBN (versión impresa)9783540734987
DOI
EstadoPublicada - 2007
Evento5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007 - Leipzig, Alemania
Duración: 18 jul. 200720 jul. 2007

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen4571 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007
País/TerritorioAlemania
CiudadLeipzig
Período18/07/0720/07/07

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