  We introduce $\texttt{time_interpret}$, a library designed as an extension of
Captum, with a specific focus on temporal data. As such, this library
implements several feature attribution methods that can be used to explain
predictions made by any Pytorch model. $\texttt{time_interpret}$ also provides
several synthetic and real world time series datasets, various PyTorch models,
as well as a set of methods to evaluate feature attributions. Moreover, while
being primarily developed to explain predictions based on temporal data, some
of its components have a different application, including for instance methods
explaining predictions made by language models. In this paper, we give a
general introduction of this library. We also present several previously
unpublished feature attribution methods, which have been developed along with
$\texttt{time_interpret}$.
