ANALYSIS OF DATA FOR FORECASTING SALES BY THE MOVING AVERAGE METHOD
Authors:
1.
Voronezh State University of Forestry and Technologies named after G.F. Morozov
2.
Voronezh State University of Forestry and Technologies named after G.F. Morozov
Russian Federation
Russian Federation
Type:
Сonference article
Pages:
from 129
to 134
Published:
30.12.2025
Subject area:
UDC 004.9
Language:
Russian
Keywords:
arbitrage, cryptocurrency, machine learning, data analysis, forecasting, least squares method, recurrent neural networks
Abstract:
The purpose of this article is to identify the capabilities and methods for analyzing data from existing and implemented offerings on cryptocurrency exchanges for use in a decision support system for arbitrage trading. Key sources of information are identified, and a methodology for selecting relevant features for mathematical models is developed based on these sources. An applicability assessment of the obtained results is conducted, demonstrating the suitability of this model for use in decision support systems.
The purpose of this article is to identify the capabilities and methods for analyzing data from existing and implemented offerings on cryptocurrency exchanges for use in a decision support system for arbitrage trading. Key sources of information are identified, and a methodology for selecting relevant features for mathematical models is developed based on these sources. An applicability assessment of the obtained results is conducted, demonstrating the suitability of this model for use in decision support systems.
Keywords:
arbitrage, cryptocurrency, machine learning, data analysis, forecasting, least squares method, recurrent neural networks
arbitrage, cryptocurrency, machine learning, data analysis, forecasting, least squares method, recurrent neural networks
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