Time series data are valuable but are often inscrutable. Gaining trust in time series classifiers for finance, healthcare, and other critical applications may rely on creating interpretable models. Researchers have previously been forced to decide between interpretable methods that lack predictive power and deep learning methods that lack transparency. In this paper, we propose a novel Mimic algorithm that retains the predictive accuracy of the strongest classifiers while introducing interpretability. Mimic mirrors the learning method of an existing multivariate time series classifier while simultaneously producing a visual representation that enhances user understanding of the learned model. Experiments on 26 time series datasets support Mimic's ability to imitate a variety of time series classifiers visually and accurately.

翻译：时间序列数据是有价值的,但往往是不可分的。 获得对财务、医疗保健和其他关键应用的时间序列分类的信任可能依赖于创建可解释模型。 研究人员以前被迫在缺乏预测力的可解释方法和缺乏透明度的深层学习方法之间做出决定。 在本文中,我们建议采用新的 Mimic 算法,保留最强分类者的预测准确性,同时引入可解释性。 Mimic 反映了现有多变时间序列分类的学习方法,同时生成视觉表达法,提高用户对所学模型的理解。 对26个时间序列数据集的实验支持 Mimic 以视觉和准确的方式模仿各种时间序列分类者的能力。