Presentation 2001/3/10
One selecting near neighbors for local linear approximation methods
Tomoya SUZUKI, Tohru IKEGUCHI, Masuo SUZUKI,
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Abstract(in English) Local linear approximation methods are one of the common tools for nonlinear prediction of chaotic time series. In the case of predicting time series data corrupted by observation noise with this method, it is possible that unsuitable points are selected as spurious neighbors, which causes lower prediction accuracy. To solve this issue, we propose a simple noise reduction method, which is based on the selection of suitable near neighbors with observational noise. In order to evaluate the proposed method, we apply local linear approximation methods to the Henon map corrupted by noise. As a result, we confirm that the noise reduction method works well to improve prediction accuracy.
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Keyword(in English) local linear approximation / prediction of chaotic time series / searching near neighbors / noise reduction
Paper # NLP2000-174
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Conference Information
Committee NLP
Conference Date 2001/3/10(1days)
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Paper Information
Registration To Nonlinear Problems (NLP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) One selecting near neighbors for local linear approximation methods
Sub Title (in English)
Keyword(1) local linear approximation
Keyword(2) prediction of chaotic time series
Keyword(3) searching near neighbors
Keyword(4) noise reduction
1st Author's Name Tomoya SUZUKI
1st Author's Affiliation Graduate School of Science, Science University of Tokyo()
2nd Author's Name Tohru IKEGUCHI
2nd Author's Affiliation Graduate School of Science and Technology, Saitama University
3rd Author's Name Masuo SUZUKI
3rd Author's Affiliation Department of Applied Physics, Faculty of Science, Science University of Tokyo
Date 2001/3/10
Paper # NLP2000-174
Volume (vol) vol.100
Number (no) 681
Page pp.pp.-
#Pages 8
Date of Issue