  This paper proposes methods of predicting dynamic time series (including
non-stationary ones) based on a linguistic approach, namely, the study of
occurrences and repetition of so-called N-grams. This approach is used in
computational linguistics to create statistical translators, detect plagiarism
and duplicate documents. However, the scope of application can be extended
beyond linguistics by taking into account the correlations of sequences of
stable word combinations, as well as trends. The proposed methods do not
require a preliminary study and determination of the characteristics of time
series or complex tuning of the input parameters of the forecasting model. They
allow, with a high level of automation, to carry out short-term and medium-term
forecasts of time series, characterized by trends and cyclicality, in
particular, series of publication dynamics in content monitoring systems. Also,
the proposed methods can be used to predict the values of the parameters of a
large complex system with the aim of monitoring its state, when the number of
such parameters is significant, and therefore a high level of automation of the
forecasting process is desirable. A significant advantage of the approach is
the absence of requirements for time series stationarity and a small number of
tuning parameters. Further research may focus on the study of various criteria
for the similarity of time series fragments, the use of nonlinear similarity
criteria, the search for ways to automatically determine the rational step of
quantization of the time series.
