  This work aims at tackling the problem of learning surrogate models from
noisy time-domain data by means of matrix pencil-based techniques, namely the
Hankel and Loewner frameworks. A data-driven approach to obtain reduced-order
state-space models from time-domain input-output measurements for linear
time-invariant (LTI) systems is proposed. This is accomplished by combining the
aforementioned model order reduction (MOR) techniques with the signal matrix
model (SMM) approach. The proposed method is illustrated by a numerical
benchmark example consisting of a building model.
