Simulating noise types (following this)
# Global options library(knitr) opts_chunk$set(fig.path="figure_noise_simulations/")
library(tuneR) # White noise w <- tuneR::noise(kind = c("white")) # Brown noise is integrated white noise # (ie. random walk) # Use same time series length as in the other series b <- cumsum(rnorm(length(w@left))) # Pink noise p <- tuneR::noise(kind = c("pink")) # Visualize par(mfrow=c(3,1)) plot(w,main="white noise") plot(b,main="brown noise") plot(p,main="pink noise")
Estimating Hurst exponent for the noises
library(pracma) Hwhite <- hurstexp(w@left, d = 128) Hbrown <- hurstexp(b, d = 128) Hpink <- hurstexp(p@left, d = 128)
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