XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
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We present XCM, an eXplainable Convolutional neural network for Multivariate time series classification. XCM is a new compact convolutional neural network which extracts, in parallel, information relative to the observed variables and time from the input data. Thus, XCM architecture enables faithful explainability based on a post-hoc model-specific method (Gradient-weighted Class Activation Mapping), which identifies 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.
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