  Dynamical processes in various natural and social phenomena have been
described by a series of events or event sequences showing non-Poissonian,
bursty temporal patterns. Temporal correlations in such bursty time series can
be understood not only by heterogeneous interevent times (IETs) but also by
correlations between IETs. Modeling and simulating various dynamical processes
requires us to generate event sequences with a heavy-tailed IET distribution
and memory effects between IETs. For this, we propose a
Farlie-Gumbel-Morgenstern copula-based algorithm for generating event sequences
with correlated IETs when the IET distribution and the memory coefficient
between two consecutive IETs are given. We successfully apply our algorithm to
the cases with heavy-tailed IET distributions. We also compare our algorithm to
the existing shuffling method to find that our algorithm outperforms the
shuffling method for some cases. Our copula-based algorithm is expected to be
used for more realistic modeling of various dynamical processes.
