XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification

Kevin Fauvel, Tao Lin, Véronique Masson, Élisa Fromont, Alexandre Termier

Published 2020-09-10, updated 2020-12-10Version 2

We present XCM, an eXplainable Convolutional neural network for Multivariate time series classification. XCM is a new compact convolutional neural network which extracts information relative to the observed variables and time directly from the input data. Thus, XCM architecture enables a good generalization ability on both small and large datasets, while allowing the full exploitation of a faithful post-hoc model-specific explainability method (Gradient-weighted Class Activation Mapping) by precisely identifying the observed variables and timestamps of the input data that are important for predictions. Our evaluation firstly shows that XCM outperforms the state-of-the-art multivariate time series classifiers on both the large and small public UEA datasets. Furthermore, following the illustration of the performance and explainability of XCM on a synthetic dataset, we present how XCM can outperform the current most accurate state-of-the-art algorithm on a real-world application while enhancing explainability by providing faithful and more informative explanations.