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The impact of interregional migration on regional economic and social sphere necessitates the determination of migration factors. An analysis of existing studies has shown that gravity models can be considered the most effective tool for migration flows analysis. At the same time existing studies show a weak elaboration of approaches that involve the separation of migration into different types depending on the goal of the relocation. The purpose of the article is to analyze the interregional migration flows in the context of types of migration identifying a set of factors that determine a particular type of migration. The main result obtained by the authors is the determination of three regional clusters that attract a certain type of migrants. Clustering was carried out using a set of indicators with a model-oriented clustering methods. The main conclusions made in the article are related to improving the quality of models when dividing regions into clusters and including different variables for certain types of migration. However, the authors note that the input data for the analysis is somewhat noisy, since it contains migrants of all ages. Therefore, the obtained regional groupings by type of migration are only the first approximation of differentiation by type of arriving or departing citizens. The results of the study can be used for further work on the definition of the determinants of interregional migration, as well as for studies on the impact of migration on regional labor markets and socioeconomic situation of regions.

The paper presents an algorithm for smoothing time series with seasonality. The proposed method is based on median smoothing and the Hodrick-Prescott decomposition. Using a software implementation in the R language, the correctness of the developed algorithm is checked; it is also compared with other seasonal smoothing methods.