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A Sequential Hybrid Forecasting System for Demand Prediction

  • Luis Aburto
  • Richard Weber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4571)

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.

Keywords

Neural Networks ARIMA Demand Forecasting Hybrid Forecasts 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Luis Aburto
    • 1
  • Richard Weber
    • 2
  1. 1.Penta Analytics, SantiagoChile
  2. 2.Department of Industrial Engineering, University ofChile

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