Reducing statistical time-series problems to binary classification

Daniil Ryabko 1 Jérémie Mary 1, 2
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
Abstract : 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.
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Daniil Ryabko, Jérémie Mary. Reducing statistical time-series problems to binary classification. NIPS, Dec 2012, Lake Tahoe, United States. pp.2069--2077. ⟨hal-00675637v5⟩

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