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Dependence of
on the order
of DFA
Another factor that affects the rms fluctuation function of the power-law trend
, is the order
of the DFA method used. We first take into account that:
- (1) for integer values of the power
, the power-law trend
is a polynomial trend which can be perfectly filtered out by the DFA method of order
, and as discussed in Sec. 3.2 and Sec. 5.1 [see Fig. 11(b) and (c)], there is no scaling for
. Therefore, in this section we consider only non-integer values of
.
- (2) for a given value of the power
, the effective exponent
can take different values depending on the order
of the DFA method we use [see Fig. 11] -- e.g. for fixed
,
. Therefore, in this section, we consider only the case when
(Region II and III).
Figure 12:
Effect of higher order DFA-
on the rms fluctuation function
for correlated noise with superimposed power-law trend. (a)
for anticorrelated noise with correlation exponent
and a power-law
, where
,
and
. Results for different order
of the DFA method show (i) a clear crossover from a region at small scales where the noise dominates
, to a region at larger scales where the power-law trend dominates
, and (ii) a vertical shift
in
with increasing
. (b) Dependence of the vertical shift
in the rms fluctuation function
for power-law trend on the order
of DFA-
for different values of
:
. We define the vertical shift
as the y-intercept of
:
. Note, that we consider only non-integer values for
and that we consider the region
. Thus, for all values of
the minimal order
that can be used in the DFA method is
. e.g. for
the minimal order of the DFA that can be used is
(for details see Fig. 11(b)).
(c) Dependence of
on the power
(error bars indicate the regression error for the fits of
in (b)).
(d) Comparison of
for
and
for
. Faster decay of
indicates larger vertical shifts for
compared to
with increasing order
of the DFA-
.
![\begin{figure}
\centerline{
\epsfysize=0.55\textwidth{\epsfbox{dev_vs_order.ep...
...ift_order.eps}}
\epsfysize=0.55\textwidth{\epsfbox{tau.eps}}}
\end{figure}](img310.png) |
Since higher order DFA-
provides a better fit for the data, the fluctuation function
(Eq. (3)) decreases with increasing order
. This leads to a vertical shift to smaller values of the rms fluctuation function
(Eq. (4)). Such a vertical shift is observed for the rms fluctuation function
for correlated noise (see Appendix 7.1), as well as for the rms fluctuation function of power-law trend
. Here we ask how this vertical shift in
and
depends on the order
of the DFA method, and if this shift has different properties for
compared to
. This information can help identify power-law trends in noisy data, and can be used to differentiate crossovers separating scaling regions with different types of correlations, and crossovers which are due to effects of power-law trends.
We consider correlated noise with a superposed power-law trend,
where the crossover in
at large scales
results from the dominant effect of the power-law trend --
(Eq. (18) and
Fig. 10(a)). We choose the power
, a
range where for all orders
of the DFA method the effective
exponent
of
remains the same --
i.e.
(region II in
Fig. 11(b)). For a superposition of an anticorrelated
noise and power-law trend with
, we observe
a crossover in the scaling behavior of
, from a
scaling region characterized by the correlation exponent
of the noise, where
, to a region characterized by an effective exponent
, where
, for all orders
of the DFA-
method
[Fig. 12(a)]. We also find that the crossover
of
shifts to larger scales when the order
of DFA-
increases, and that there is a vertical shift
of
to lower values. This vertical shift in
at large scales, where
, appears to be different in
magnitude when different order
of the DFA-
method is
used [Fig. 12(a)]. We also
observe a less pronounced vertical shift at small scales where
.
Next, we ask how these vertical shifts depend on
the order
of DFA-
. We define the vertical shift
as the y-intercept of
:
. We find that the vertical shift
in
for power-law trend follows a power law:
. We tested this relation
for orders up to
, and we find that it holds for
different values of the power
of the power-law trend
[Fig. 12(b)]. Using Eq. (19) we can write:
, i.e.
. Since
[Fig. 12(b)], we find that:
![\begin{displaymath}
F_{\rm P}(n) \sim \ell^{\tau(\lambda)}.
\end{displaymath}](img318.png) |
(20) |
We also find that the exponent
is negative and is a decreasing function of the power
[Fig. 12(c)]. Because the effective
exponent
which characterizes
depends on the power
[see Fig. 11(b)], we
can express the exponent
as a function of
as we show in Fig. 12(d).
This representation can help us compare the behavior of the
vertical shift
in
with the shift in
. For correlated noise with different correlation
exponent
, we observe a similar power-law relation
between the vertical shift in
and the order
of DFA-
:
, where
is also a negative exponent which decreases with
.
In Fig. 12(d) we compare
for
with
for
, and find that for any
,
. This difference between the vertical shift for
correlated noise and for a power-law trend can be utilized to
recognize effects of power-law trends on the scaling properties of
data.
Next: Dependence of on the
Up: Noise with Power-law trends
Previous: Dependence of on the
Zhi Chen
2002-08-28