Determination of Marketing Parameters for Building a Demand Forecasting Model using Neural Networks

Authors

  • Victor Sineglazov National Aviation University, Kyiv, Ukraine https://orcid.org/0000-0002-3297-9060
  • Mikhaylo Novikov National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

DOI:

https://doi.org/10.18372/1990-5548.78.18263

Keywords:

determination of marketing parameters, forecasting, neural networks, regression models, multilayer perceptron

Abstract

This article is dedicated to the discovery of marketing parameters for the implementation of a forecasting model using additional neural measures using data from real data. The robot looks at the problem in the field of modeling the delivery of goods to the market in marketing using additional methods of artificial intelligence and machine learning. The main features of the main approaches to introducing product models to the market, their advantages and disadvantages are shown. It has been revealed that they will require thoroughness. A new methodology for solving the problem is presented. The model's ability to accurately predict lasting demand based on a variety of marketing parameters has been demonstrated, which helps businesses effectively plan inventory, inventory, and personnel, and can lead to significant cost savings. and increased efficiency.

Author Biographies

Victor Sineglazov , National Aviation University, Kyiv, Ukraine

Doctor of Engineering Science

Professor

Head of the Department Aviation Computer-Integrated Complexes

Faculty of Air Navigation Electronics and Telecommunications

Mikhaylo Novikov , National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,”

Graduate Student

Department of Artificial Intelligence

Institute of Applied System Analysis

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Published

2023-12-27

How to Cite

Sineglazov , V., & Novikov , M. (2023). Determination of Marketing Parameters for Building a Demand Forecasting Model using Neural Networks. Electronics and Control Systems, 4(78), 44–51. https://doi.org/10.18372/1990-5548.78.18263

Issue

Vol. 4 No. 78 (2023)

Section

COMPUTER SCIENCES AND INFORMATION TECHNOLOGIES

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