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Optimized Demand Forecasting by Cross-Validation

In: New Approaches in Social and Humanistic Sciences

Author

Listed:
  • Răzvan Daniel ZOTA

    (The Bucharest University of Economic Studies, Bucharest, Romania)

  • Yasser AL HADAD

    (The Bucharest University of Economic Studies, Bucharest, Romania)

Abstract

Sales forecasting plays an important role in business strategy. An appropriate demand forecasting model is necessary for reducing the cost of storage. At a company level, lowering the warehouse costs and optimizing the value chain is a prominent requirement for an optimum stock management. In this paper a demand forecasting model is built to support the stock management activity of medium enterprises by means of data mining algorithms. SQL server analysis service is used for implementing the demand forecasting model. The paper studies a list of available algorithms that are offered by SQL server analysis service and the performance of the aforementioned algorithms is tested using the cross-validation feature that is provided by SQL server analysis service to optimize the performance of the model. We also aim to explore in our research the ability of RMSE (Root mean Squared Error) to include time series algorithms in the cross-validation phase. The proposed model is tested based on a dataset of a timber export company and the output is used for analysing the performance of the proposed model. The paper reached a group of conclusion and one of most the importance conclusion is neural network algorithms performance was the better in adapting our tested dataset comparing with the other algorithms.

Suggested Citation

  • Răzvan Daniel ZOTA & Yasser AL HADAD, 2018. "Optimized Demand Forecasting by Cross-Validation," Book chapters-LUMEN Proceedings, in: Veaceslav MANOLACHI & Cristian Mihail RUS & Svetlana RUSNAC (ed.), New Approaches in Social and Humanistic Sciences, edition 1, volume 3, chapter 50, pages 563-574, Editura Lumen.
    • Handle: RePEc:lum:prchap:03-50
    DOI: https://doi.org/10.18662/lumproc.nashs2017.50
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    References listed on IDEAS

    as
    1. Ovidiu-Alin DOBRICAN, 2013. "Forecasting Demand for Automotive Aftermarket Inventories," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 17(2), pages 119-129.
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    1. Răzvan Daniel ZOTA & Yasser AL HADAD, 2018. "Inventory Management Using Cross Prediction," Book chapters-LUMEN Proceedings, in: Veaceslav MANOLACHI & Cristian Mihail RUS & Svetlana RUSNAC (ed.), New Approaches in Social and Humanistic Sciences, edition 1, volume 3, chapter 51, pages 575-585, Editura Lumen.

    More about this item

    Keywords

    Demand forecasting; BI (Business intelligence); SAS (SQL analysis services); cross-validation; data analysis;
    All these keywords.

    JEL classification:

    • A3 - General Economics and Teaching - - Multisubject Collective Works
    • I2 - Health, Education, and Welfare - - Education
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General

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