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 We also present several previously unpublished feature attribution methods, which have been developed along with $\texttt{time_interpret}$.

翻译：我们介绍了一个名为 $\texttt{time_interpret}$ 的库，该库设计为 Captum 的扩展，专门针对时序数据。为此，该库实现了多种特征归因方法，可用于解释任何 PyTorch 模型做出的预测。$\texttt{time_interpret}$ 还提供了多个合成和真实世界的时间序列数据集、各类 PyTorch 模型，以及一套评估特征归因的方法。此外，尽管其主要开发目的是基于时序数据解释预测结果，但其部分组件具有不同的应用场景，例如包括解释语言模型预测结果的方法。在本文中，我们对该库进行了总体介绍。同时，我们还将介绍几种此前未发表的特征归因方法，这些方法是与 $\texttt{time_interpret}$ 一同开发的。