TSInterpret: A unified framework for time series interpretability

2022/08/10 by Jacqueline Höllig, Höllig, Jacqueline, Cedric Kulbach +3 · 2 citations
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.LG

paper · pdf · doi:10.48550/arxiv.2208.05280

arxiv created 2022/08/15 · arxiv updated 2022/08/16

Abstract

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.

Cited by

• Counterfactual Explanations for Time Series Should be Human-Centered and Temporally Coherent in Interventions
• Guided by Stars: Interpretable Concept Learning Over Time Series via Temporal Logic Semantics

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