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Publikācija: Fractal Brownian Motion Analysis Using Continuous Wavelet Transform

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Nosaukums oriģinālvalodā Fractal Brownian Motion Analysis Using Continuous Wavelet Transform
Pētniecības nozare 1. Dabaszinātnes
Pētniecības apakšnozare 1.1. Matemātika
Autori Andrejs Pučkovs
Andrejs Matvejevs
Atslēgas vārdi Fractal Brownian motion, Direct Continuous Wavelet Transform, Inverse Continuous Wavelet Transform, probability density function, Mexican hat mother wavelet function, Hurst exponent, Normal distribution, time series
Anotācija This article is dedicated for Fractal Brownian process analysis using Continuous Wavelet Transform (Direct and Inverse). Wavelet Analysis of stochastic processes is very important for financial time series analysis, risk estimation and financial time series forecasting. Wavelet Analysis is very precious for scalability analysis, because of its ability to analyze the signal (process) in scaling and shifting dimensions. In current research, Fractal Brownian motion is analyzed using Direct and Inverse Continuous Wavelet Transform, wavelet coefficients probability density function is estimated, wavelet coefficients lower and upper bounds are calculated using Mexican hat mother wavelet function. At the end estimation results are illustrated.
Hipersaite: http://www.journal.aplimat.com/ 
Atsauce Pučkovs, A., Matvejevs, A. Fractal Brownian Motion Analysis Using Continuous Wavelet Transform. APLIMAT, 2013, Vol.6, 59.-70.lpp. ISSN 1337-6365.
Papildinformācija Citējamību skaits:
  • Scopus  0
ID 18433