  In order to solve partial differential equations numerically and accurately,
a high order spatial discretization is usually needed. Model order reduction
(MOR) techniques are often used to reduce the order of spatially-discretized
systems and hence reduce computational complexity. A particular class of MOR
techniques are $\mathcal H_2$-optimal methods such as the iterative rational
Krylov subspace algorithm (IRKA) and related schemes. However, these methods
are used to obtain good approximations on a infinite time-horizon. Thus, in
this work, our main goal is to discuss MOR schemes for time-limited linear
systems. For this, we propose an alternative time-limited $\mathcal H_2$-norm
and show its connection with the time-limited Gramians. We then provide
first-order optimality conditions for an optimal reduced order model (ROM) with
respect to the time-limited $\mathcal H_2$-norm. Based on these optimality
conditions, we propose an iterative scheme, which, upon convergence, aims at
satisfying these conditions approximately. Then, we analyze how far away the
obtained ROM due to the proposed algorithm is from satisfying the optimality
conditions. We test the efficiency of the proposed iterative scheme using
various numerical examples and illustrate that the newly proposed iterative
method can lead to a better reduced-order compared to the unrestricted IRKA in
the finite time interval of interest.
