论文标题
tsinterpret：时间序列可解释性的统一框架
TSInterpret: A unified framework for time series interpretability
论文作者
论文摘要
随着深度学习算法在时间序列分类中的应用越来越多，尤其是在高风化场景中，解释这些算法的相关性就成为关键。尽管时间序列的可解释性研究已经增强，但从业者的可访问性仍然是一个障碍。没有统一的API或框架，使用的可解释性方法及其可视化的使用方式多种多样。为了缩小此差距，我们引入了TSInterpret易于扩展的开源Python库，用于解释将现有解释方法结合到一个统一框架中的时间序列分类器的预测。该库具有（i）最新的可解释性算法，（ii）公开了统一的API，使用户能够始终如一地使用解释，并为每种说明提供合适的可视化。
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.
