Standard

Time series prediction based on data compression methods. / Lysyak, A. S.; Ryabko, B. Ya.

In: Problems of Information Transmission, Vol. 52, No. 1, 01.01.2016, p. 92-99.

Research output: Contribution to journal › Article › peer-review

Harvard

Lysyak, AS & Ryabko, BY 2016, 'Time series prediction based on data compression methods', Problems of Information Transmission, vol. 52, no. 1, pp. 92-99. https://doi.org/10.1134/S0032946016010075

APA

Lysyak, A. S., & Ryabko, B. Y. (2016). Time series prediction based on data compression methods. Problems of Information Transmission, 52(1), 92-99. https://doi.org/10.1134/S0032946016010075

Vancouver

Lysyak AS, Ryabko BY. Time series prediction based on data compression methods. Problems of Information Transmission. 2016 Jan 1;52(1):92-99. doi: 10.1134/S0032946016010075

Author

Lysyak, A. S. ; Ryabko, B. Ya. / Time series prediction based on data compression methods. In: Problems of Information Transmission. 2016 ; Vol. 52, No. 1. pp. 92-99.

BibTeX

@article{abec31ff2a7a4866a7fd0ac4836ef26a,
title = "Time series prediction based on data compression methods",
abstract = "We propose efficient (“fast” and low memory consuming) algorithms for universal-coding-based prediction methods for real-valued time series. Previously, for such methods it was only proved that the prediction error is asymptotically minimal, and implementation complexity issues have not been considered at all. The provided experimental results demonstrate high precision of the proposed methods.",
author = "Lysyak, {A. S.} and Ryabko, {B. Ya}",
year = "2016",
month = jan,
day = "1",
doi = "10.1134/S0032946016010075",
language = "English",
volume = "52",
pages = "92--99",
journal = "Problems of Information Transmission",
issn = "0032-9460",
publisher = "Maik Nauka-Interperiodica Publishing",
number = "1",

}

RIS

TY - JOUR

T1 - Time series prediction based on data compression methods

AU - Lysyak, A. S.

AU - Ryabko, B. Ya

PY - 2016/1/1

Y1 - 2016/1/1

N2 - We propose efficient (“fast” and low memory consuming) algorithms for universal-coding-based prediction methods for real-valued time series. Previously, for such methods it was only proved that the prediction error is asymptotically minimal, and implementation complexity issues have not been considered at all. The provided experimental results demonstrate high precision of the proposed methods.

AB - We propose efficient (“fast” and low memory consuming) algorithms for universal-coding-based prediction methods for real-valued time series. Previously, for such methods it was only proved that the prediction error is asymptotically minimal, and implementation complexity issues have not been considered at all. The provided experimental results demonstrate high precision of the proposed methods.

UR - http://www.scopus.com/inward/record.url?scp=84966388998&partnerID=8YFLogxK

U2 - 10.1134/S0032946016010075

DO - 10.1134/S0032946016010075

M3 - Article

AN - SCOPUS:84966388998

VL - 52

SP - 92

EP - 99

JO - Problems of Information Transmission

JF - Problems of Information Transmission

SN - 0032-9460

IS - 1

ER -

ID: 25331260