The further development of society directly depends on the use of technologies connected with processing data arrays and identifying patterns with the help of computer means. In this study, machine learning methods allowed us to analyze technological trends using large open-source data on patents, which enable predicting future skills in demand in the labor market. It is of major importance in the context of the rapid development of technology, leading to large-scale technological changes that transform the social conditions of human life as a whole, the requirements for the skills of people, which in the future will cause the emergence of new specialties and the disappearance of existing professions. For this purpose, predictive regression models of groups of patents according to the International Patent Classification are built using machine-learning methods — classical forecasting methods, such as naive forecasting, simple exponential smoothing, and ARIMA. As a result of comparing the quality of the constructed models and choosing the best one, ARIMA models were identified, showing “fading” technologies if there is a decrease in the number of patents; promising technological directions if the growth is stable; or “breakthrough” technologies if there has been a sharp increase in recent years. The input variables of the models were the series of dynamics of patents of different classes in the form of historical data, the output variables were the predicted values of the number of patents of these classes of a certain technological trend. The algorithm was implemented in the high-level Python programming language. The research results will enable authorities, employers, educational institutions, etc. to make a forecast of the demand for existing, as well as new professional skills and competencies in the labor market.