Nonparametric multiple change point estimation in highly dependent time series

Azadeh Khaleghi 1 Daniil Ryabko 1
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : Given a heterogeneous time-series sample, it is required to find the points in time (called change points) where the probability distribution generating the data has changed. The data is assumed to have been generated by arbitrary, unknown, stationary ergodic distributions. No modeling, independence or mixing 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.
Type de document :
Communication dans un congrès
Proc. 24th International Conf. on Algorithmic Learning Theory (ALT'13), 2013, Singapore, Singapore. Springer, pp.382-396, 2013, LNCS 8139
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https://hal.inria.fr/hal-00913250
Contributeur : Daniil Ryabko <>
Soumis le : mardi 3 décembre 2013 - 14:28:55
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13

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  • HAL Id : hal-00913250, version 1

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Azadeh Khaleghi, Daniil Ryabko. Nonparametric multiple change point estimation in highly dependent time series. Proc. 24th International Conf. on Algorithmic Learning Theory (ALT'13), 2013, Singapore, Singapore. Springer, pp.382-396, 2013, LNCS 8139. 〈hal-00913250〉

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