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An evaluation of forecasting methods and forecast combination methods in goods management systems

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  • Schneider, Carsten
  • Klapper, Matthias
  • Wenzel, Thomas

Abstract

In this paper we use 4 different time series models to forecast sales in a goods management system. We use a variety of forecast combining techniques and measure the forecast quality by applying symmetric and asymmetric forecast quality measures. Simple, rank-, and criteria-based combining methods lead to an improvement of the individual time series models.

Suggested Citation

  • Schneider, Carsten & Klapper, Matthias & Wenzel, Thomas, 1999. "An evaluation of forecasting methods and forecast combination methods in goods management systems," Technical Reports 1999,31, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:199931
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    File URL: https://www.econstor.eu/bitstream/10419/77356/2/1999-31.pdf
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    References listed on IDEAS

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    1. Thomas D. Russell & Everett E. Adam, Jr., 1987. "An Empirical Evaluation of Alternative Forecasting Combinations," Management Science, INFORMS, vol. 33(10), pages 1267-1276, October.
    2. Klapper, Matthias, 1998. "Combining German macro economic forecasts using rank-based techniques," Technical Reports 1998,19, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Arminger, Gerhard & Schneider, Carsten, 1999. "Frequent problems of model specification and forecasting of time series in goods management systems," Technical Reports 1999,21, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Thomas Wenzel, 2001. "Hits-and-misses for the evaluation and combination of forecasts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(6), pages 759-773.
    2. Wenzel, Thomas, 2000. "Hits-and-misses for the evaluation and combination of forecasts," Technical Reports 2000,26, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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