Deep neural networks are one of the most successful classifiers across different domains. However, due to their limitations concerning interpretability their use is limited in safety critical context. The research field of explainable artificial intelligence addresses this problem. However, most of the interpretability methods are aligned to the image modality by design. The paper introduces TimeREISE a model agnostic attribution method specifically aligned to success in the context of time series classification. The method shows superior performance compared to existing approaches concerning different well-established measurements. TimeREISE is applicable to any time series classification network, its runtime does not scale in a linear manner concerning the input shape and it does not rely on prior data knowledge.

翻译：深神经网络是不同领域最成功的分类方法之一,但是,由于在安全关键方面对可解释性使用的限制,这些网络的使用有限。可以解释的人工智能研究领域解决这一问题。然而,大多数可解释性方法都通过设计与图像模式相一致。文件引入了时间序列分类方面与成功特别一致的模型识别归属方法。该方法显示与关于不同既定测量的现有方法相比,业绩优异。时间序列分类网络适用于任何时间序列分类网络,其运行时间不以输入形状线性的方式缩放,也不依赖先前的数据知识。