  The superfamily phenomenon of time series with different dynamics can be
characterized by the motif rank patterns observed in the nearest-neighbor
networks of the time series in phase space. However, the determinants of
superfamily classification are unclear. We attack this problem by studying the
influence of linear temporal correlations and multifractality using fractional
Brownian motions (FBMs) and multifractal random walks (MRWs). Numerical
investigations unveil that the classification of superfamily phenomenon is
uniquely determined by the detrended fluctuation analysis (DFA) scaling
exponent $\alpha$ of the time series. Only four motif patterns are observed in
the simulated data, which are delimited by three DFA scaling exponents $\alpha
\simeq 0.25$, $\alpha \simeq 0.35$ and $\alpha \simeq 0.45$. The validity of
the result is confirmed by stock market indexes and turbulence velocity
signals.
