Open AccessMODELING THE POVERTY LEVEL IN THE RUSSIAN FEDERATION BASED ON A NEURAL NETWORK APPROACH
Author(s) -
Margarita Yuryevna Karlova,
E. Ryazanceva
Publication year - 2021
Publication title -
aktualʹnye napravleniâ naučnyh issledovanij xxi veka: teoriâ i praktika
Language(s) - English
Resource type - Journals
ISSN - 2308-8877
DOI - 10.34220/2308-8877-2021-9-3-142-156
Subject(s) - poverty , artificial neural network , relevance (law) , computer science , strengths and weaknesses , econometrics , quality (philosophy) , artificial intelligence , sampling (signal processing) , russian federation , operations research , regional science , machine learning , management science , economics , engineering , political science , geography , economic growth , telecommunications , psychology , social psychology , philosophy , epistemology , detector , law
The article raises the question of modeling the level of poverty as one of the most important socio-economic indicators. A review of publications by domestic and foreign scientists-economists proves the relevance of the topic chosen for the study. Today, the time series apparatus acts as one of the popular tools for studying the dynamics of the poverty level and the factors that directly influence it, but classical statistical forecasting methods impose rather strict assumptions on the construction of models. The article discusses the possibility of using automated neural networks of the STATISTICA package for analyzing and forecasting a time series composed of annual data reflecting the dynamics of the poverty level in the Russian Federation over the past 20 years. The study took into account the strengths and weaknesses of the use of the neural network apparatus for predicting socio-economic processes. The construction of economic and mathematical models was carried out by building automated neural networks, custom neural networks and the method of multiple sampling. When choosing the most preferable model, a multidimensional criterion was used. The comparison of the real poverty level with the values obtained using the models is made, the quality assessment of the developed models is calculated, the poverty level forecast for 2021-2022 is constructed.