  Characterizing inhomogeneous temporal patterns in natural and social
phenomena is important to understand underlying mechanisms behind such complex
systems, hence even to predict and control them. Temporal inhomogeneities in
event sequences have been described in terms of bursts that are rapidly
occurring events in short time periods alternating with long inactive periods.
The bursts can be quantified by a simple measure, called burstiness parameter,
which was introduced by Goh and Barab\'asi [EPL \textbf{81}, 48002 (2008)]. The
burstiness parameter has been widely used due to its simplicity, which however
turns out to be strongly affected by the finite number of events in the time
series. As the finite-size effects on burstiness parameter have been largely
ignored, we analytically investigate the finite-size effects of the burstiness
parameter. Then we suggest an alternative definition of burstiness that is free
from finite-size effects and yet simple. Using our alternative burstiness
measure, one can distinguish the finite-size effects from the intrinsic bursty
properties in the time series. We also demonstrate the advantages of our
burstiness measure by analyzing empirical datasets.
