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Clustering piecewise stationary processes

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Clustering piecewise stationary processes. / Khaleghi, Azadeh; Ryabko, Daniil.
2020 IEEE International Symposium on Information Theory. IEEE, 2020. p. 2753-2758.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Khaleghi, A & Ryabko, D 2020, Clustering piecewise stationary processes. in 2020 IEEE International Symposium on Information Theory. IEEE, pp. 2753-2758. https://doi.org/10.1109/ISIT44484.2020.9174045

APA

Khaleghi, A., & Ryabko, D. (2020). Clustering piecewise stationary processes. In 2020 IEEE International Symposium on Information Theory (pp. 2753-2758). IEEE. https://doi.org/10.1109/ISIT44484.2020.9174045

Vancouver

Khaleghi A, Ryabko D. Clustering piecewise stationary processes. In 2020 IEEE International Symposium on Information Theory. IEEE. 2020. p. 2753-2758 doi: 10.1109/ISIT44484.2020.9174045

Author

Khaleghi, Azadeh ; Ryabko, Daniil. / Clustering piecewise stationary processes. 2020 IEEE International Symposium on Information Theory. IEEE, 2020. pp. 2753-2758

Bibtex

@inproceedings{707f4c3d426a42ffb86a07c9f9fb1405,
title = "Clustering piecewise stationary processes",
abstract = "The problem of time-series clustering is considered in the case where each data-point is a sample generated by a piecewise stationary process. While stationary processes comprise one of the most general classes of processes in nonparametric statistics, and in particular, allow for arbitrary long-range dependencies, their key assumption of stationarity remains restrictive for some applications.We address this shortcoming by considering piecewise stationary processes, studied here for the first time in the context of clustering. It turns out that this problem allows for a rather natural definition of consistency of clustering algorithms. Efficient algorithms are proposed which are shown to be asymptotically consistent without any additional assumptions beyond piecewise stationarity. The theoretical results are complemented with experimental evaluations.",
author = "Azadeh Khaleghi and Daniil Ryabko",
note = "{\textcopyright}2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2020",
month = aug,
day = "24",
doi = "10.1109/ISIT44484.2020.9174045",
language = "English",
isbn = "9781728164335",
pages = "2753--2758",
booktitle = "2020 IEEE International Symposium on Information Theory",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Clustering piecewise stationary processes

AU - Khaleghi, Azadeh

AU - Ryabko, Daniil

N1 - ©2020 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2020/8/24

Y1 - 2020/8/24

N2 - The problem of time-series clustering is considered in the case where each data-point is a sample generated by a piecewise stationary process. While stationary processes comprise one of the most general classes of processes in nonparametric statistics, and in particular, allow for arbitrary long-range dependencies, their key assumption of stationarity remains restrictive for some applications.We address this shortcoming by considering piecewise stationary processes, studied here for the first time in the context of clustering. It turns out that this problem allows for a rather natural definition of consistency of clustering algorithms. Efficient algorithms are proposed which are shown to be asymptotically consistent without any additional assumptions beyond piecewise stationarity. The theoretical results are complemented with experimental evaluations.

AB - The problem of time-series clustering is considered in the case where each data-point is a sample generated by a piecewise stationary process. While stationary processes comprise one of the most general classes of processes in nonparametric statistics, and in particular, allow for arbitrary long-range dependencies, their key assumption of stationarity remains restrictive for some applications.We address this shortcoming by considering piecewise stationary processes, studied here for the first time in the context of clustering. It turns out that this problem allows for a rather natural definition of consistency of clustering algorithms. Efficient algorithms are proposed which are shown to be asymptotically consistent without any additional assumptions beyond piecewise stationarity. The theoretical results are complemented with experimental evaluations.

U2 - 10.1109/ISIT44484.2020.9174045

DO - 10.1109/ISIT44484.2020.9174045

M3 - Conference contribution/Paper

SN - 9781728164335

SP - 2753

EP - 2758

BT - 2020 IEEE International Symposium on Information Theory

PB - IEEE

ER -