@inproceedings{44b62572c03645188b64eeab8f674a9e,
title = "A sequential hybrid forecasting system for demand prediction",
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 = "ARIMA, Demand forecasting, Hybrid forecasts, Neural networks",
author = "Luis Aburto and Richard Weber",
note = "Funding Information: This work was supported in part by the One-Hundred-Talent Program and the National Natural Science Foundation of China (grants 10543003 and 10573029).; 5th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2007 ; Conference date: 18-07-2007 Through 20-07-2007",
year = "2007",
doi = "10.1007/978-3-540-73499-4_39",
language = "English",
isbn = "9783540734987",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "518--532",
booktitle = "Machine Learning and Data Mining in Pattern Recognition - 5th International Conference, MLDM 2007, Proceedings",
}