Establishment of Nonlinear ARMA Model for Non-Gaussian Stochastic Process and Its Application to Time Series Data of Road Traffic Noise

Akira IKUTA; Mitsuo OHTA

Establishment of Nonlinear ARMA Model for Non-Gaussian Stochastic Process and Its Application to Time Series Data of Road Traffic Noise

Akira IKUTA, Mitsuo OHTA

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In the actual acoustic environment, the stochastic process exhibits various non-Gaussian distribution forms, and there exist potentially various nonlinear correlations in addition to the linear correlation between time series. In this study, a nonlinear ARMA model is proposed, based on the Bayes' theorem, where no artificially pre-established regression function model is assumed between time series, while reflecting hierarchically all of those various correlation informations. The proposed method is applied to the actual data of road traffic noise and its practical usefulness is verified.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E77-A No.8 pp.1345-1352

Publication Date: 1994/08/25

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Type of Manuscript: Special Section PAPER (Special Section on Information Theory and Its Applications)

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Akira IKUTA, Mitsuo OHTA, "Establishment of Nonlinear ARMA Model for Non-Gaussian Stochastic Process and Its Application to Time Series Data of Road Traffic Noise" in IEICE TRANSACTIONS on Fundamentals, vol. E77-A, no. 8, pp. 1345-1352, August 1994, doi: .
Abstract: In the actual acoustic environment, the stochastic process exhibits various non-Gaussian distribution forms, and there exist potentially various nonlinear correlations in addition to the linear correlation between time series. In this study, a nonlinear ARMA model is proposed, based on the Bayes' theorem, where no artificially pre-established regression function model is assumed between time series, while reflecting hierarchically all of those various correlation informations. The proposed method is applied to the actual data of road traffic noise and its practical usefulness is verified.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e77-a_8_1345/_p

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@ARTICLE{e77-a_8_1345,
author={Akira IKUTA, Mitsuo OHTA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Establishment of Nonlinear ARMA Model for Non-Gaussian Stochastic Process and Its Application to Time Series Data of Road Traffic Noise},
year={1994},
volume={E77-A},
number={8},
pages={1345-1352},
abstract={In the actual acoustic environment, the stochastic process exhibits various non-Gaussian distribution forms, and there exist potentially various nonlinear correlations in addition to the linear correlation between time series. In this study, a nonlinear ARMA model is proposed, based on the Bayes' theorem, where no artificially pre-established regression function model is assumed between time series, while reflecting hierarchically all of those various correlation informations. The proposed method is applied to the actual data of road traffic noise and its practical usefulness is verified.},
keywords={},
doi={},
ISSN={},
month={August},}

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TY - JOUR
TI - Establishment of Nonlinear ARMA Model for Non-Gaussian Stochastic Process and Its Application to Time Series Data of Road Traffic Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1345
EP - 1352
AU - Akira IKUTA
AU - Mitsuo OHTA
PY - 1994
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E77-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1994
AB - In the actual acoustic environment, the stochastic process exhibits various non-Gaussian distribution forms, and there exist potentially various nonlinear correlations in addition to the linear correlation between time series. In this study, a nonlinear ARMA model is proposed, based on the Bayes' theorem, where no artificially pre-established regression function model is assumed between time series, while reflecting hierarchically all of those various correlation informations. The proposed method is applied to the actual data of road traffic noise and its practical usefulness is verified.
ER -