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Journal Articles Theoretical Computer Science Year : 2016

Nonparametric multiple change point estimation in highly dependent time series

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Abstract

Given a heterogeneous time-series sample, the objective is to find points in time, called change points, where the probability distribution generating the data has changed. The data are assumed to have been generated by arbitrary unknown stationary ergodic distributions. No modelling, independence or mixing assumptions are made. A novel, computationally efficient, nonparametric method is proposed, and is shown to be asymptotically consistent in this general framework. The theoretical results are complemented with experimental evaluations.

Dates and versions

hal-01235330 , version 1 (30-11-2015)

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Azadeh Khaleghi, Daniil Ryabko. Nonparametric multiple change point estimation in highly dependent time series. Theoretical Computer Science, 2016, 620, pp.119-133. ⟨10.1016/j.tcs.2015.10.041⟩. ⟨hal-01235330⟩
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