TSInterpret: A unified framework for time series interpretability
This work addresses the problem of fragmented interpretability tools for practitioners in high-stake time series classification applications, though it is incremental as it combines existing methods.
The authors tackled the lack of accessibility and standardization in time series interpretability by introducing TSInterpret, an open-source Python library that unifies existing interpretation approaches into a single framework with a consistent API and visualizations.
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.