The growth and reduction of cycle times is a characteristic feature of modern economy. Increasingly, cyclical factor is cause of imbalance and violation of global and national economies stability. However, the question of regional industry cycles asynchrony has not been studied. It was previously believed that the regional industry cycle, its stages of growth and decline, is consistent with the national one. The work shows that it is precisely the asynchrony of regional industrial cycles that underlies imbalance and reduces stability of country's economic development. The study presented by the authors expands boundaries of understanding the laws of recessions and upswings in industry cycles, since it reveals the causes and consequences of asynchronism of industry cycles in Russian regions. Terminologically, the regional sectoral cycle should be interpreted as a dynamic process of fluctuations in economic activity within life cycle of industry, characterized by repeatability of successive stages of decline and rise in the region's industry. The authors developed a classification of main parameters of regional industry cycle according to their cyclic properties, including leading, lagging and coinciding indicators of the cycle dynamics. A generalization of scientific and methodological approaches to industry cycles as a risk factor for sustainable and balanced development of regions is presented. The causes and consequences of regional sectoral cycles for the sustainability of regional economy are determined. The measures proposed for regional authorities to prevent the negative consequences of phase shift of sectoral regional cycle will be of practical importance.

The differentiation in the development of regions remains a major challenge for the working out-of-state industrial and regional policies aimed at balanced and sustainable development. In theory, regional differences between internal and border regions can be explained by differences in natural resources, and economic and industrial potential, as well as by the existence of external boundaries. Border regions have higher risks in ensuring the geo-political sustainability of an industry. External boundaries, as well as differences in industry dynamics between regions, cycle stages, and industry trends, are often overlooked in industrial policy making, which in itself can be a factor of volatility. In this research based upon the Russian economy, we test the hypothesis that it is possible to define the industrial cycle with the help of the index of production. The analysis is based on the official Russian statistics from January 2005 to December 2021. To test the hypothesis, an original 12-step method of analysis was used, which allows such a mathematical model to be selected that will best describe the industry cycle and allows the trend to be estimated. The cyclic dynamics were assessed with the help of structural and parametric identification of modeling and the forecasting of trajectories of evolving dynamics based upon econophysics methodology, the use of median trends, and wavelet analysis. The comparative study was made based on the example of four sectors: the food, chemical, pharmaceutical (production of medicines and materials used for medical purposes), and automotive industries. The results show, first, that there are significant differences in the dynamics of industry cycles in both the internal and the border regions, which need to be taken into account to implement the progressive economic structure and specialization strategies of a region. Secondly, the group of border regions in the food, chemical, and pharmaceutical industries is growing at a higher rate.

The article is devoted to the author’s approach and tools for regional industries’ modeling, analysis and forecasting, following the general idea of splitting time series into four components: trend, cycles, seasonal component, and residuals. However, the authors introduce new approaches, models, metrics, and identification algorithms, and the components’ interaction structures, having included the analysis of 12 industries in 82 regions of Russia. The models and forecast accuracy were tested on 3–12 month forecasts, thus proving their high accuracy. Therefore, the article proposes not only new systematic econometric tools but a methodology for decision making, developed to provide stable and adequate characteristics of complex non-linear evolutionary dynamics of Russian regions.