Identification of seasonal fluctuations in the nonlinear dynamics of the number of registered crimes

Abstract

Subject. The article considers trends in different types of crimes committed in the Russian Federation from 2012 to 2019. Objectives. The purpose is to determine trends and the presence or absence of annual seasonality in the analyzed dynamics. Methods. The study draws on parametric modeling of trend-seasonal dynamics, using our own procedures, a set of models and methods for their identification by means of generalized ARMA models, the STL (Seasonal Transformation using LOESS) method, the Yeo-Johnson method based on standard libraries and applications of the R programming language. Results. The paper offers two methods to model seasonality: a "rough" assessment of its presence and a "fine" assessment, with obtaining quantitative estimates of model parameters and estimates of qualitative characteristics of modeling. We determine optimal smoothing settings to solve the problem of trend-seasonal modeling of crime dynamics, analyze the dynamics of eleven types of registered crimes, and identify the parameters of seasonal component for each of them. Conclusions. In nine out of eleven types of considered crimes, there is a pronounced annual seasonality, which is advisable to take into account, when organizing and planning the law enforcement activities.