ML Anthology

Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series

Azadeh Khaleghi, Daniil Ryabko
ALT 2013 pp. 382-396
doi:10.1007/978-3-642-40935-6_27 /alt/2013/khaleghi2013alt-nonparametric/

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 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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Cite

Text

Khaleghi and Ryabko. "Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series." International Conference on Algorithmic Learning Theory, 2013. doi:10.1007/978-3-642-40935-6_27

Markdown

[Khaleghi and Ryabko. "Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series." International Conference on Algorithmic Learning Theory, 2013.](https://mlanthology.org/alt/2013/khaleghi2013alt-nonparametric/) doi:10.1007/978-3-642-40935-6_27

BibTeX

@inproceedings{khaleghi2013alt-nonparametric,
  title     = {{Nonparametric Multiple Change Point Estimation in Highly Dependent Time Series}},
  author    = {Khaleghi, Azadeh and Ryabko, Daniil},
  booktitle = {International Conference on Algorithmic Learning Theory},
  year      = {2013},
  pages     = {382-396},
  doi       = {10.1007/978-3-642-40935-6_27},
  url       = {https://mlanthology.org/alt/2013/khaleghi2013alt-nonparametric/}
}