dfa.SlidingWindows {SlidingWindows}R Documentation

Detrended Fluctuation Analysis with sliding windows.

Description

This function generates scaling exponents (long-range correlations) of a univariate time series with sliding windows approach.

Usage

dfa.SlidingWindows(y, w = 98, k = 10, npoints = 15)

Arguments

y

A vector containing univariate time series.

w

An integer value indicating the window size w<length(y)w < length(y). If w=length(y)w = length(y), will be computed the function will not slide.

k

An integer value indicating the boundary of the division (N/k)(N/k). The smallest value of kk is 44.

npoints

The number of different time scales that will be used to estimate the Fluctuation function in each zone. See nonlinearTseries package.

Details

This function include following measures: alpha_dfa, se_alpha_dfa, r2_alpha_dfa.

Value

A list contaning "w", "alpha_dfa", "se_alpha_dfa", "r2_alpha_dfa".

References

GUEDES, E.F.;FERREIRA, P.;DIONISIO, A.; ZEBENDE,G.F. An econophysics approach to study the effect of BREXIT referendum on European Union stock markets. PHYSICA A, v.523, p.1175-1182, 2019. doi = "doi.org/10.1016/j.physa.2019.04.132".

FERREIRA, P.; DIONISIO, A.;GUEDES, E.F.; ZEBENDE, G.F. A sliding windows approach to analyse the evolution of bank shares in the European Union. PHYSICA A, v.490, p.1355-1367, 2018. doi = "doi.org/10.1016/j.physa.2017.08.095".

Examples

y <- rnorm(100)
dfa.SlidingWindows(y,w=99,k=10,npoints=15)


[Package SlidingWindows version 0.2.0 Index]