Nonparametric multiple change point estimation in highly dependent time series

Azadeh Khaleghi 1 Daniil Ryabko 2
2 SEQUEL - Sequential Learning
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
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.
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Theoretical Computer Science, Elsevier, 2016, 620, pp.119-133. 〈10.1016/j.tcs.2015.10.041〉
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https://hal.inria.fr/hal-01235330
Contributeur : Daniil Ryabko <>
Soumis le : lundi 30 novembre 2015 - 09:07:09
Dernière modification le : mardi 3 juillet 2018 - 11:33:49

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

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