IEICE TRANSACTIONS on Fundamentals
Predictability of Iteration Method for Chaotic Time Series
Summary :
The approximation expression about error accumulation of a long-term prediction is derived. By analyzing this formula, we find that the factors that can affect the long-term predictability include the model parameters, prediction errors and the derivates of the used basis functions. To enlarge the maximum attempting time, we present that more suitable basis functions should be those with smaller derivative functions and a fast attenuation where out of the time series range. We compare the long-term predictability of a non-polynomial based algorithm and a polynomial one to prove the success of our method.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.4 pp.840-842
- Publication Date
- 2010/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E93.A.840
- Type of Manuscript
- LETTER
- Category
- Nonlinear Problems
Authors
Keyword
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Yun BU, Guang-jun WEN, Hai-Yan JIN, Qiang ZHANG, "Predictability of Iteration Method for Chaotic Time Series" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 4, pp. 840-842, April 2010, doi: 10.1587/transfun.E93.A.840.
Abstract: The approximation expression about error accumulation of a long-term prediction is derived. By analyzing this formula, we find that the factors that can affect the long-term predictability include the model parameters, prediction errors and the derivates of the used basis functions. To enlarge the maximum attempting time, we present that more suitable basis functions should be those with smaller derivative functions and a fast attenuation where out of the time series range. We compare the long-term predictability of a non-polynomial based algorithm and a polynomial one to prove the success of our method.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.840/_p
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@ARTICLE{e93-a_4_840,
author={Yun BU, Guang-jun WEN, Hai-Yan JIN, Qiang ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Predictability of Iteration Method for Chaotic Time Series},
year={2010},
volume={E93-A},
number={4},
pages={840-842},
abstract={The approximation expression about error accumulation of a long-term prediction is derived. By analyzing this formula, we find that the factors that can affect the long-term predictability include the model parameters, prediction errors and the derivates of the used basis functions. To enlarge the maximum attempting time, we present that more suitable basis functions should be those with smaller derivative functions and a fast attenuation where out of the time series range. We compare the long-term predictability of a non-polynomial based algorithm and a polynomial one to prove the success of our method.},
keywords={},
doi={10.1587/transfun.E93.A.840},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - Predictability of Iteration Method for Chaotic Time Series
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 840
EP - 842
AU - Yun BU
AU - Guang-jun WEN
AU - Hai-Yan JIN
AU - Qiang ZHANG
PY - 2010
DO - 10.1587/transfun.E93.A.840
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2010
AB - The approximation expression about error accumulation of a long-term prediction is derived. By analyzing this formula, we find that the factors that can affect the long-term predictability include the model parameters, prediction errors and the derivates of the used basis functions. To enlarge the maximum attempting time, we present that more suitable basis functions should be those with smaller derivative functions and a fast attenuation where out of the time series range. We compare the long-term predictability of a non-polynomial based algorithm and a polynomial one to prove the success of our method.
ER -
English
