Mimic: An adaptive algorithm for multivariate time series classification
This addresses the need for trustworthy models in critical domains like finance and healthcare, though it is incremental as it builds on existing classifiers.
The paper tackles the trade-off between interpretability and predictive accuracy in multivariate time series classification by proposing the Mimic algorithm, which retains the accuracy of strong classifiers while providing visual interpretability, as demonstrated on 26 datasets.
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.