  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.
