A Binary-Classification-Based Metric between Time-Series Distributions and Its Use in Statistical and Learning Problems
Daniil Ryabko, Jérémie Mary; 14(50):2837−2856, 2013.
A metric between time-series distributions is proposed that can be evaluated using binary classification methods, which were originally developed to work on i.i.d. data. It is shown how this metric 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. Universal consistency of the resulting algorithms is proven under most general assumptions. The theoretical results are illustrated with experiments on synthetic and real-world data.
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