Standard
Nonparametric multiple change point estimation in highly dependent time series. /
Khaleghi, Azedeh; Ryabko, Daniil.
Algorithmic Learning Theory: 224th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings. ed. / Sanjay Jain; Rémi Munos; Frank Stephan; Thomas Zeugmann. Cham: Springer, 2013. p. 382-396 (Lecture Notes in Computer Science; Vol. 8139).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Khaleghi, A & Ryabko, D 2013,
Nonparametric multiple change point estimation in highly dependent time series. in S Jain, R Munos, F Stephan & T Zeugmann (eds),
Algorithmic Learning Theory: 224th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8139, Springer, Cham, pp. 382-396, Algorithmic Learning Theory (ALT), Singapore, Singapore,
6/10/13.
https://doi.org/10.1007/978-3-642-40935-6_27
APA
Khaleghi, A., & Ryabko, D. (2013).
Nonparametric multiple change point estimation in highly dependent time series. In S. Jain, R. Munos, F. Stephan, & T. Zeugmann (Eds.),
Algorithmic Learning Theory: 224th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings (pp. 382-396). (Lecture Notes in Computer Science; Vol. 8139). Springer.
https://doi.org/10.1007/978-3-642-40935-6_27
Vancouver
Khaleghi A, Ryabko D.
Nonparametric multiple change point estimation in highly dependent time series. In Jain S, Munos R, Stephan F, Zeugmann T, editors, Algorithmic Learning Theory: 224th International Conference, ALT 2013, Singapore, October 6-9, 2013. Proceedings. Cham: Springer. 2013. p. 382-396. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-40935-6_27
Author
Bibtex
@inproceedings{3fcb512b5cb6410da5db04f09705d5de,
title = "Nonparametric multiple change point estimation in highly dependent time series",
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.",
keywords = "change-point analysis, stationary ergodic time series, unsupervised learning, consistency",
author = "Azedeh Khaleghi and Daniil Ryabko",
year = "2013",
month = sep,
doi = "10.1007/978-3-642-40935-6_27",
language = "English",
isbn = "9783642409349",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "382--396",
editor = "Sanjay Jain and R{\'e}mi Munos and Frank Stephan and Thomas Zeugmann",
booktitle = "Algorithmic Learning Theory",
note = "Algorithmic Learning Theory (ALT) ; Conference date: 06-10-2013",
}
RIS
TY - GEN
T1 - Nonparametric multiple change point estimation in highly dependent time series
AU - Khaleghi, Azedeh
AU - Ryabko, Daniil
PY - 2013/9
Y1 - 2013/9
N2 - 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.
AB - 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.
KW - change-point analysis
KW - stationary ergodic time series
KW - unsupervised learning
KW - consistency
U2 - 10.1007/978-3-642-40935-6_27
DO - 10.1007/978-3-642-40935-6_27
M3 - Conference contribution/Paper
SN - 9783642409349
T3 - Lecture Notes in Computer Science
SP - 382
EP - 396
BT - Algorithmic Learning Theory
A2 - Jain, Sanjay
A2 - Munos, Rémi
A2 - Stephan, Frank
A2 - Zeugmann, Thomas
PB - Springer
CY - Cham
T2 - Algorithmic Learning Theory (ALT)
Y2 - 6 October 2013
ER -
