Markov models for linguistic sequences

  • Ihor Vsevolodovych Baklan
  • Tetiana Viktorivna Shulkevych


The popularity of hidden Markov models of fuels and lubricants and their implementation in various fields, spreads every year, leads to certain problems.
The aim of the study is the use of hidden Markov models for the analysis of time series in the form of linguistic chains.
This study has its own goal of identifying the problems facing the developers of intelligent systems with the use of fuel and lubricants and identifying some of the areas in which these problems can be overcome. For the whole family of standard fuels and lubricants, three main problems were identified, the solution of which is very important for analyzing and forecasting time series.
Today, hidden Markov models is one of the most common mathematical tools used for many classifiers and modeling of various problems. In recent years, fuels and lubricants are used for gesture recognition. It is clear that this article does not provide a complete list of the problems facing the developers of intelligent systems using fuel, but it is a definite step towards the integration of modern methods for solving complex problems.


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