The long-term development of the world economy is characterized by cyclical development. To date, there is no single accepted approach to describe the nature of the economic cycle. Therefore, studies of economic and political cycles are one of the key areas of economic theory. Econometrics and machine learning have a common goal: to build a predictive model, for a target variable, using explanatory variables. This research aims to identify economic cycle in Russian Federation using collective factors. It uses a different approach, concerning classical econometric techniques, and shows how machine learning (ML) techniques can improve the accuracy of forecasts. We used three machine learning algorithms such as k-Nearest Neighbors (kNN), Random Forests (RF) and Support vector machines (SVM). The research is based on 30 economic factors for the period 1990-2020 from FRED, World Bank, WTO, Federal State Statistics Service, Bank of Russia etc. The results indicate that the Russian economy would be very active (peak) in the next quarters. This result could be a new approach to provide policy recommendations to authorities and financial institutions in particular.
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Brou Kouame Amos, Ivan V. Smirnov, Irina A. Aidrous, Ravil R. Asmyatullin, Sofia G. Glavina. Economic Cycle Prediction using Machine Learning - Russia Case Study // Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2022. St. Petersburg. 2022.