Applies the MultiFractal Detrended Fluctuation Analysis (MFDFA) to time series. The package contains some suggestion plot of the MFDFA results.
The MFDFA R library is now available on CRAN. Further update will be added soon.
A new file is available Here. It proposes the MFDFA with a parallel version (MFDFA2.R). Useful for long time series. It can be used as the first one with same parameters. It uses (N-1) of CPU cores of your computer.
Use the following to get it:
devtools::source_gist("bb0c09df9593dad16ae270334ec3e7d7", filename = "MFDFA2.r")
ENJOY ...
1.1
Mohamed Laib, Luciano Telesca and Mikhail Kanevski
Mohamed Laib [mohamed.laib (at) unil.ch] or [laib.med (at) gmail.com]
https://cran.r-project.org/package=MFDFA
https://mlaib.github.io/MFDFA/
GPL-3
If the codes are used in scientific publications please cite the following:
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M. Laib, L. Telesca, M. Kanevski, Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network, Chaos: An Interdisciplinary Journal of Nonlinear Science 28 (3), 033108. Paper
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M. Laib, J. Golay, L. Telesca, M. Kanevski, Multifractal analysis of the time series of daily means of wind speed in complex regions, Chaos, Solitons & Fractals, 109 (2018) pp. 118-127. Paper
install.packages("devtools")
devtools::install_github("mlaib/MFDFA")
library(MFDFA)
a<-0.9
N<-1024
tsx<-MFsim(N,a)
scale=10:100
q<--10:10
m<-1
mfdfa<-MFDFA(tsx, scale, m, q)
dev.new()
par(mai=rep(1, 4))
plot(q, mfdfa$Hq, col=1, axes= F, ylab=expression('h'[q]), pch=16, cex.lab=1.8,
cex.axis=1.8, main="Hurst exponent",
ylim=c(min(mfdfa$Hq),max(mfdfa$Hq)))
grid(col="midnightblue")
axis(1)
axis(2)
library(MFDFA)
a<-0.9
N<-10000
tsx<-MFsim(N,a)
scale=10:1000
q<--10:10
m<-1
system.time(mfdfa<-MFDFA(tsx, scale, m, q))
# ~ 47.60 s
devtools::source_gist("bb0c09df9593dad16ae270334ec3e7d7", filename = "MFDFA2.r")
system.time(mfdfa<-MFDFA2(tsx, scale, m, q))
# ~ 12s