Presentation 2002/8/28
Feature Extraction of Time Series by using the Multifractal Signals and its Applications
Noboru TAKAGI, Shozo TOKINAGA,
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Abstract(in English) This report deals with feature extraction of time series based on the multifractal and its applications. We introduce the representation of multifractal by using the wavelet coefficients in contrast to the conventional definition in the time domain. The relation among the multifractal and the moments of wavelet coefficients for the time series is derived. The higher-order moments of wavelet coefficient are easy to calculate , and we can have precise information for each dilation index m. As an application, the basic prescribed waveforms such as the fractal time series and sinusoidal waves are recognized by the system, and also the system is applied to the feature extraction of the time series of stock trends.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Multifractal / Wavelet Coefficients / High-order Moments / Feature Extraction / Time Series Recognition
Paper # NLP2002-61
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Committee NLP
Conference Date 2002/8/28(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) Feature Extraction of Time Series by using the Multifractal Signals and its Applications
Sub Title (in English)
Keyword(1) Multifractal
Keyword(2) Wavelet Coefficients
Keyword(3) High-order Moments
Keyword(4) Feature Extraction
Keyword(5) Time Series Recognition
1st Author's Name Noboru TAKAGI
1st Author's Affiliation Graduate School of Economics, Kyushu University()
2nd Author's Name Shozo TOKINAGA
2nd Author's Affiliation Graduate School od Economics, Kyushu University
Date 2002/8/28
Paper # NLP2002-61
Volume (vol) vol.102
Number (no) 298
Page pp.pp.-
#Pages 6
Date of Issue