ts_sumorder | R Documentation |
Many fluctuation analyses assume a time series' Hurst exponent is within the range of 0.2 - 1.2
. If this is not the case it is sensible to make adjustments to the time series, as well as the resutling Hurst exponent.
ts_sumorder(y, scaleS = NULL, polyOrder = 1, dataMin = 4)
y |
A time series of numeric vector |
scaleS |
The scales to consider for |
polyOrder |
Order of polynomial for detrending in DFA (default = |
dataMin |
Minimum number of data points in a bin needed to calculate detrended fluctuation |
Following recommendations by https://www.frontiersin.org/files/Articles/23948/fphys-03-00141-r2/image_m/fphys-03-00141-t001.jpgIhlen (2012), a global Hurst exponent is estimated using DFA and y
is adjusted accordingly:
1.2 < H < 1.8
first derivative of y, atribute Hadj = 1
H > 1.8
second derivative of y, atribute Hadj = 2
H < 0.2
y is centered and integrated, atribute Hadj = -1
0.2 <= H <= 1.2
y is unaltered, atribute Hadj = 0
The input vector, possibly adjusted based on H
with an attribute "Hadj"
containing an integer by which a Hurst exponent calculated from the series should be adjusted.
Ihlen, E. A. F. E. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in physiology, 3, 141.
Other Time series operations:
ts_center()
,
ts_changeindex()
,
ts_checkfix()
,
ts_detrend()
,
ts_diff()
,
ts_discrete()
,
ts_duration()
,
ts_embed()
,
ts_integrate()
,
ts_levels()
,
ts_peaks()
,
ts_permtest_block()
,
ts_permtest_transmat()
,
ts_rasterize()
,
ts_sd()
,
ts_slice()
,
ts_slopes()
,
ts_standardise()
,
ts_symbolic()
,
ts_trimfill()
,
ts_windower()
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