  With the increasing application of deep learning algorithms to time series
classification, especially in high-stake scenarios, the relevance of
interpreting those algorithms becomes key. Although research in time series
interpretability has grown, accessibility for practitioners is still an
obstacle. Interpretability approaches and their visualizations are diverse in
use without a unified API or framework. To close this gap, we introduce
TSInterpret an easily extensible open-source Python library for interpreting
predictions of time series classifiers that combines existing interpretation
approaches into one unified framework. The library features (i)
state-of-the-art interpretability algorithms, (ii) exposes a unified API
enabling users to work with explanations consistently and provides (iii)
suitable visualizations for each explanation.
