Prediction of Chaotic Time Series with Noise

Tohru IKEGUCHI; Kazuyuki AIHARA

Prediction of Chaotic Time Series with Noise

Tohru IKEGUCHI, Kazuyuki AIHARA

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In this paper, we propose algorithm of deterministic nonlinear prediction, or a modified version of the method of analogues which was originally proposed by E.N. Lorenz (J. Atom. Sci., 26, 636-646, 1969), and apply it to the artificial time series data produced from nonlinear dynamical systems and further corrupted by superimposed observational noise. The prediction performance of the present method are investigated by calculating correlation coefficients, root mean square errors and signature errors and compared with the prediction algorithm of local linear approximation method. As a result, it is shown that the prediction performance of the proposed method are better than those of the local linear approximation especially in case that the amount of noise is large.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.10 pp.1291-1298

Publication Date: 1995/10/25

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

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Tohru IKEGUCHI, Kazuyuki AIHARA, "Prediction of Chaotic Time Series with Noise" in IEICE TRANSACTIONS on Fundamentals, vol. E78-A, no. 10, pp. 1291-1298, October 1995, doi: .
Abstract: In this paper, we propose algorithm of deterministic nonlinear prediction, or a modified version of the method of analogues which was originally proposed by E.N. Lorenz (J. Atom. Sci., 26, 636-646, 1969), and apply it to the artificial time series data produced from nonlinear dynamical systems and further corrupted by superimposed observational noise. The prediction performance of the present method are investigated by calculating correlation coefficients, root mean square errors and signature errors and compared with the prediction algorithm of local linear approximation method. As a result, it is shown that the prediction performance of the proposed method are better than those of the local linear approximation especially in case that the amount of noise is large.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_10_1291/_p

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@ARTICLE{e78-a_10_1291,
author={Tohru IKEGUCHI, Kazuyuki AIHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Prediction of Chaotic Time Series with Noise},
year={1995},
volume={E78-A},
number={10},
pages={1291-1298},
abstract={In this paper, we propose algorithm of deterministic nonlinear prediction, or a modified version of the method of analogues which was originally proposed by E.N. Lorenz (J. Atom. Sci., 26, 636-646, 1969), and apply it to the artificial time series data produced from nonlinear dynamical systems and further corrupted by superimposed observational noise. The prediction performance of the present method are investigated by calculating correlation coefficients, root mean square errors and signature errors and compared with the prediction algorithm of local linear approximation method. As a result, it is shown that the prediction performance of the proposed method are better than those of the local linear approximation especially in case that the amount of noise is large.},
keywords={},
doi={},
ISSN={},
month={October},}

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TY - JOUR
TI - Prediction of Chaotic Time Series with Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1291
EP - 1298
AU - Tohru IKEGUCHI
AU - Kazuyuki AIHARA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 1995
AB - In this paper, we propose algorithm of deterministic nonlinear prediction, or a modified version of the method of analogues which was originally proposed by E.N. Lorenz (J. Atom. Sci., 26, 636-646, 1969), and apply it to the artificial time series data produced from nonlinear dynamical systems and further corrupted by superimposed observational noise. The prediction performance of the present method are investigated by calculating correlation coefficients, root mean square errors and signature errors and compared with the prediction algorithm of local linear approximation method. As a result, it is shown that the prediction performance of the proposed method are better than those of the local linear approximation especially in case that the amount of noise is large.
ER -