Mimic: An adaptive algorithm for multivariate time series classification

TL;DR

Mimic is an innovative algorithm that combines high predictive accuracy with interpretability for multivariate time series classification, making models more understandable without sacrificing performance.

Contribution

It introduces a novel method that visually mimics existing classifiers, enhancing interpretability while maintaining accuracy in multivariate time series tasks.

Findings

Supports 26 datasets demonstrating Mimic’s effectiveness

Accurately imitates various classifiers visually

Maintains high predictive performance

Abstract

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.