  In this work a method for statistical analysis of time series is proposed,
which is used to obtain solutions to some classical problems of mathematical
statistics under the only assumption that the process generating the data is
stationary ergodic. Namely, three problems are considered: goodness-of-fit (or
identity) testing, process classification, and the change point problem. For
each of the problems a test is constructed that is asymptotically accurate for
the case when the data is generated by stationary ergodic processes. The tests
are based on empirical estimates of distributional distance.
