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On Locally Dyadic Stationary Processes

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On Locally Dyadic Stationary Processes. / Moysiadis, T.; Fokianos, K.
In: IEEE Transactions on Information Theory, Vol. 63, No. 8, 08.2017, p. 4829-4837.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Moysiadis, T & Fokianos, K 2017, 'On Locally Dyadic Stationary Processes', IEEE Transactions on Information Theory, vol. 63, no. 8, pp. 4829-4837. https://doi.org/10.1109/TIT.2016.2631143

APA

Moysiadis, T., & Fokianos, K. (2017). On Locally Dyadic Stationary Processes. IEEE Transactions on Information Theory, 63(8), 4829-4837. https://doi.org/10.1109/TIT.2016.2631143

Vancouver

Moysiadis T, Fokianos K. On Locally Dyadic Stationary Processes. IEEE Transactions on Information Theory. 2017 Aug;63(8):4829-4837. Epub 2016 Nov 21. doi: 10.1109/TIT.2016.2631143

Author

Moysiadis, T. ; Fokianos, K. / On Locally Dyadic Stationary Processes. In: IEEE Transactions on Information Theory. 2017 ; Vol. 63, No. 8. pp. 4829-4837.

Bibtex

@article{a61fde493a0c4603ba1f45f148b51937,
title = "On Locally Dyadic Stationary Processes",
abstract = "We introduce the concept of local dyadic stationarity, to account for nonstationary time series, within the framework of Walsh-Fourier analysis. We define and study time-varying, dyadic, autoregressive, moving average (tvDARMA) models. It is proven that the general tvDARMA process can be approximated locally by either a time-varying dyadic moving average and a time-varying dyadic autoregressive processes.",
author = "T. Moysiadis and K. Fokianos",
year = "2017",
month = aug,
doi = "10.1109/TIT.2016.2631143",
language = "English",
volume = "63",
pages = "4829--4837",
journal = "IEEE Transactions on Information Theory",
issn = "0018-9448",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "8",

}

RIS

TY - JOUR

T1 - On Locally Dyadic Stationary Processes

AU - Moysiadis, T.

AU - Fokianos, K.

PY - 2017/8

Y1 - 2017/8

N2 - We introduce the concept of local dyadic stationarity, to account for nonstationary time series, within the framework of Walsh-Fourier analysis. We define and study time-varying, dyadic, autoregressive, moving average (tvDARMA) models. It is proven that the general tvDARMA process can be approximated locally by either a time-varying dyadic moving average and a time-varying dyadic autoregressive processes.

AB - We introduce the concept of local dyadic stationarity, to account for nonstationary time series, within the framework of Walsh-Fourier analysis. We define and study time-varying, dyadic, autoregressive, moving average (tvDARMA) models. It is proven that the general tvDARMA process can be approximated locally by either a time-varying dyadic moving average and a time-varying dyadic autoregressive processes.

U2 - 10.1109/TIT.2016.2631143

DO - 10.1109/TIT.2016.2631143

M3 - Journal article

VL - 63

SP - 4829

EP - 4837

JO - IEEE Transactions on Information Theory

JF - IEEE Transactions on Information Theory

SN - 0018-9448

IS - 8

ER -