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Neural Network Associative Forecasting of Demand for Goods

Author

Listed:
  • Osipov, Vasiliy
  • Zhukova, Nataly
  • Miloserdov, Dmitriy

Abstract

This article discusses the applicability of recurrent neural networks with controlled elements to the problem of forecasting market demand for goods on the four month horizon. Two variants of forecasting are considered. In the first variant, time series are used to train the neural network, including the real demand values, as well as pre-order values for 1, 2 and 3 months ahead. In the second variant, there is an iterative forecasting method. It predicts the de-mand for the next month at each step, and the training set is supplemented by the values predicted for the previous months. It is shown that the proposed methods can give a sufficiently high result. At the same time, the second ap-proach demonstrates greater potential.

Suggested Citation

  • Osipov, Vasiliy & Zhukova, Nataly & Miloserdov, Dmitriy, 2019. "Neural Network Associative Forecasting of Demand for Goods," MPRA Paper 97314, University Library of Munich, Germany, revised 23 Sep 2019.
  • Handle: RePEc:pra:mprapa:97314
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    More about this item

    Keywords

    Recurrent Neural Network; Machine Learning; Data Mining; Demand Forecasting;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General

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