A Sequential Hybrid Forecasting System for Demand Prediction

Authors:
Luis Aburto;Richard Weber
Affiliations:
Penta Analytics, Santiago, Chile;Department of Industrial Engineering, University of, Chile
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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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.