The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Propusemos um novo modelo para análise de séries temporais não estacionárias baseado em uma equação AR (autoregressiva) não homogênea. Os dados da série temporal são considerados como ruído branco mais a saída de um sistema AR excitado por uma sequência de entrada não estacionária representada em termos de um conjunto de bases. Um método de estimação dos parâmetros do modelo foi apresentado quando o conjunto de bases e a ordem AR são dados. Para estender o método, apresentamos um método de estimativa de parâmetros quando a ordem AR é desconhecida: definimos dois novos critérios 1) minimizar a raiz do erro quadrático médio da sequência de saída e 2) minimizar o espalhamento das frequências estimadas. Em seguida, derivamos um procedimento para a estimativa da ordem AR e dos demais parâmetros desconhecidos.
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Yukiko YOKOYAMA, Mineo KUMAZAWA, Naoki MIKAMI, "Estimation of the AR Order of an Inhomogeneous AR Model with Input Expanded by a Set of Basis" in IEICE TRANSACTIONS on Fundamentals,
vol. E83-A, no. 3, pp. 551-557, March 2000, doi: .
Abstract: We proposed a new model for non-stationary time series analysis based on an inhomogeneous AR (autoregressive) equation. Time series data is regarded as white noise plus output of an AR system excited by non-stationary input sequence represented in terms of a set of basis. A method of model parameter estimation was presented when the set of basis and the AR order are given. In order to extend the method, we present a method of parameter estimation when the AR order is unknown: we set two new criteria 1) minimize the root mean square error of the output sequence, and 2) minimize scattering of estimated frequencies. Then, we derive a procedure for the estimation of the AR order and the other unknown parameters.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e83-a_3_551/_p
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@ARTICLE{e83-a_3_551,
author={Yukiko YOKOYAMA, Mineo KUMAZAWA, Naoki MIKAMI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Estimation of the AR Order of an Inhomogeneous AR Model with Input Expanded by a Set of Basis},
year={2000},
volume={E83-A},
number={3},
pages={551-557},
abstract={We proposed a new model for non-stationary time series analysis based on an inhomogeneous AR (autoregressive) equation. Time series data is regarded as white noise plus output of an AR system excited by non-stationary input sequence represented in terms of a set of basis. A method of model parameter estimation was presented when the set of basis and the AR order are given. In order to extend the method, we present a method of parameter estimation when the AR order is unknown: we set two new criteria 1) minimize the root mean square error of the output sequence, and 2) minimize scattering of estimated frequencies. Then, we derive a procedure for the estimation of the AR order and the other unknown parameters.},
keywords={},
doi={},
ISSN={},
month={March},}
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TY - JOUR
TI - Estimation of the AR Order of an Inhomogeneous AR Model with Input Expanded by a Set of Basis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 551
EP - 557
AU - Yukiko YOKOYAMA
AU - Mineo KUMAZAWA
AU - Naoki MIKAMI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E83-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2000
AB - We proposed a new model for non-stationary time series analysis based on an inhomogeneous AR (autoregressive) equation. Time series data is regarded as white noise plus output of an AR system excited by non-stationary input sequence represented in terms of a set of basis. A method of model parameter estimation was presented when the set of basis and the AR order are given. In order to extend the method, we present a method of parameter estimation when the AR order is unknown: we set two new criteria 1) minimize the root mean square error of the output sequence, and 2) minimize scattering of estimated frequencies. Then, we derive a procedure for the estimation of the AR order and the other unknown parameters.
ER -