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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
장기예측의 오차누적에 대한 근사식을 유도한다. 이 공식을 분석함으로써 우리는 장기 예측 가능성에 영향을 미칠 수 있는 요소에는 모델 매개변수, 예측 오류 및 사용된 기본 함수의 파생물이 포함된다는 것을 발견했습니다. 최대 시도 시간을 늘리기 위해 더 적합한 기본 함수는 시계열 범위를 벗어나는 더 작은 미분 함수와 빠른 감쇠를 갖는 함수여야 함을 제시합니다. 우리는 우리 방법의 성공을 증명하기 위해 비다항식 기반 알고리즘과 다항식 알고리즘의 장기 예측 가능성을 비교합니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -