  A validated simulation model primarily requires performing an appropriate
input analysis mainly by determining the behavior of real-world processes using
probability distributions. In many practical cases, probability distributions
of the random inputs vary over time in such a way that the functional forms of
the distributions and/or their parameters depend on time. This paper answers
the question whether a sequence of observations from a process follow the same
statistical distribution, and if not, where the exact change points are, so
that observations within two consecutive change points follow the same
distribution. We propose two different methods based on likelihood ratio test
and cluster analysis to detect multiple change points when observations follow
non-stationary Poisson process with diverse occurrence rates over time. Results
from a comprehensive Monte Carlo study indicate satisfactory performance for
the proposed methods. A well-known example is also considered to show the
application of our findings in real world cases.
