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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.
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Contributor : Daniil Ryabko <>
Submitted on : Tuesday, December 3, 2013 - 2:28:55 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:20 PM


  • HAL Id : hal-00913250, version 1


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. pp.382-396. ⟨hal-00913250⟩



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