AI-system and multifactor correlation-regression model for forecasting RF GDP in the Colab and XL

Abstract

The article discusses the theoretical foundations for forecasting the gross domestic product (GDP) and the sustainability of the development of the Russian economy. The forecast values of GDP were calculated by two methods: based on the use of the AI-system and a multivariate correlationregression model for the purpose of subsequent comparison. The novelty of the study lies in the fact that a comparative assessment of the accuracy of the generated forecasted values of the RF GDP by different methods was carried out: using the AI system and a multivariate correlation-regression model in XLtables using the Microsoft Correlation and Regression packages. Some of the calculations were carried out in the Colab cloud environment using the Python programming language. The initial data for the AI-system and the correlation-regression model are statistical data — chain indices of the development of the main industries taken for the period 2000–2020. Studies have shown that the average forecast error calculated using a linear six-factor regression model between factorial features and the effective one was 0.153553 %, while the average forecast error calculated using the AI system was 0.010082 %. The forecasting accuracy using artificial intelligence algorithms is an order of magnitude higher than the traditional one — using a multivariate regression model. Based on the initial data, a dataset was collected, on which, using the Deductor analytical platform, an AI system was formed in order to obtain the forecast value of the RF GDP for the next calendar year. Using the capabilities of the Colab cloud service, in the Python language, the pairwise correlation coefficients between the factors were calculated, the results of which are presented in a heat matrix. Formed a correlation-regression model in XL-tables using the packages "Correlation" and "Regression" Microsoft. Also, the quality of the regression model, reflecting the dependence of the effective indicator "GDP" on the main factorial indicators, was assessed using the Student’s t-test, F-statistics and others.