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The Use of Generalized Additive Models for Demand Forecasting
Linköping University, Department of Management and Engineering. Linköping University, The Institute of Technology.
2010 (English)Conference paper, Published paper (Other academic)
Abstract [en]

Forecasting using time series  is often based on linear regression. In this paper, we use theGeneralized Additive Models (sum of smooth functions) instead of the linear regression to analyze the behavior of the system in the demand forecasting of  make-to-stock problem. Main focus is on the existence of the nonparametric analogue of multicollinearity, concurvity in sales data. This provides insight in to managing interdependent demands. We also investigate whether the specific temporal patterns are likely to affect the statistical forecasts of the sales history

Place, publisher, year, edition, pages
2010. 2- p.
Series
Book of abstracts Lisbon July 11-14, 2010
Keyword [en]
Data Mining Forecasting Supply Chain Management
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-70414OAI: oai:DiVA.org:liu-70414DiVA: diva2:439166
Conference
24th European Conference on Operational Research, 11-14 juli, Lissabon, Portugal
Available from: 2011-09-06 Created: 2011-09-06 Last updated: 2011-09-13

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http://www.euro2010lisbon.org/

Authority records BETA

Sadoghi, Amirhossein

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CiteExportLink to record
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  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
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Output format
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