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:
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
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
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