  We show how binary classification methods developed to work on i.i.d. data
can be used for solving statistical problems that are seemingly unrelated to
classification and concern highly-dependent time series. Specifically, the
problems of time-series clustering, homogeneity testing and the three-sample
problem are addressed. The algorithms that we construct for solving these
problems are based on a new metric between time-series distributions, which can
be evaluated using binary classification methods. Universal consistency of the
proposed algorithms is proven under most general assumptions. The theoretical
results are illustrated with experiments on synthetic and real-world data.
