Physics – Data Analysis – Statistics and Probability
Scientific paper
2006-07-26
Phys. Rev. E 74, 051122 2006
Physics
Data Analysis, Statistics and Probability
10 pages, 13 figures, submitted to Physical Review E
Scientific paper
10.1103/PhysRevE.74.051122
We address the generic problem of extracting the scaling exponents of a stationary, self-affine process realised by a timeseries of finite length, where information about the process is not known a priori. Estimating the scaling exponents relies upon estimating the moments, or more typically structure functions, of the probability density of the differenced timeseries. If the probability density is heavy tailed, outliers strongly influence the scaling behaviour of the moments. From an operational point of view, we wish to recover the scaling exponents of the underlying process by excluding a minimal population of these outliers. We test these ideas on a synthetically generated symmetric alpha-stable Levy process and show that the Levy exponent is recovered in up to the 6th order moment after only ~0.1-0.5% of the data are excluded. The scaling properties of the excluded outliers can then be tested to provide additional information about the system.
Chapman Sandra C.
Hnat Bogdan
Kiyani Khurom
No associations
LandOfFree
A method for extracting the scaling exponents of a self-affine, non-Gaussian process from a finite length timeseries does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with A method for extracting the scaling exponents of a self-affine, non-Gaussian process from a finite length timeseries, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A method for extracting the scaling exponents of a self-affine, non-Gaussian process from a finite length timeseries will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-477949