Journal of Process Management and New Technologies (Dec 2022)

APPLICABILITY OF MACHINE LEARNING MODELS USING A NEURAL NETWORK FOR PREDICTING THE PARAMETERS OF THE DEVELOPMENT OF FOOD MARKETS

  • Alexander Dubovitski,
  • Elvira Klimentova,
  • Matvei Rogov

DOI
https://doi.org/10.5937/jpmnt10-41317
Journal volume & issue
Vol. 10, no. 3-4
pp. 93 – 105

Abstract

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Forecasting the parameters of the food market is a difficult task due to the volatility of demand, which depends on many factors. In this study, the authors attempted to implement a machine learning model based on multiple data on the food market. A boxed recurrent neural network was chosen as a prediction technique. The information basis was made up of data from 3,200 US cities for 2010-2012, reflecting characteristics that may be directly or indirectly related to the price of dairy products. The following models were used for data preprocessing, anomaly search, dimensionality reduction: AdaBoost, LogisticRegression, SVM. As a result of analytical actions, a neural network architecture has been formed for use in market forecasting: two competitive neural networks. First: 2 layers with Bidirectional GRU+Dropout. Second: 3 layers of LSTM+Dropout + Attention with skip-layers. Its use makes it possible to obtain a prediction model of the desired parameters with qualitative indicators of the validation sample - R^= 0.86. The applicability of the constructed machine learning model is considered on the example of classical agricultural production with the presentation of the stages of deployment of such a model at the enterprise level.

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