Time Interpret: a Unified Model Interpretability Library for Time Series
This library addresses the need for interpretability tools in time series analysis, offering a unified solution for researchers and practitioners, but it is incremental as it builds on existing frameworks.
The authors introduced time_interpret, a library extending Captum for interpretability in time series models, providing feature attribution methods, datasets, models, and evaluation tools, and presented several new attribution methods developed with it.
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 We also present several previously unpublished feature attribution methods, which have been developed along with $\texttt{time_interpret}$.