  Forecasts of various processes have always been a sophisticated problem for
statistics and data science. Over the past decades the solution procedures were
updated by deep learning and kernel methods. According to many specialists,
these approaches are much more precise, stable, and suitable compared to the
classical statistical linear time series methods. Here we investigate how true
this point of view is.
