Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)


For citation:

Khokhlova O. A., Khokhlova A. N. Analysis of technological trends to identify skills that will be in demand in the labor market with open-source data using machine learning methods. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2022, vol. 22, iss. 1, pp. 123-129. DOI: 10.18500/1816-9791-2022-22-1-123-129, EDN: OJTLCA

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.03.2022
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English
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Article type: 
Article
UDC: 
004.85
EDN: 
OJTLCA

Analysis of technological trends to identify skills that will be in demand in the labor market with open-source data using machine learning methods

Autors: 
Khokhlova Oksana Anatolyevna, East Siberian State University of Technology and Management
Abstract: 

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.

References: 
  1. Brownlee J. Comparing Classical and Machine Learning Algorithms for Time Series Forecasting. Available at: https://machinelearningmastery.com/findings-comparing-classical-and-machine-learning-methods-for-time-series-forecasting/ (accessed 28 August 2021).
  2. Kuhn M., Johnson K. Applied Predictive Modeling. New York, Springer Science+Business Media, 2013. 600 p. https://doi.org/10.1007/978-1-4614-6849-3
Received: 
07.12.2021
Accepted: 
21.12.2021
Published: 
31.03.2022