XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
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DOI：
10.3390/math9233137
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发表时间：
2021-12-01
期刊：
影响因子：
2.4
通讯作者：
Termier, Alexandre
中科院分区：
文献类型：
--
作者：
Fauvel, Kevin;Lin, Tao;Termier, Alexandre
随着多领域时间数据集数量的增加，多变量时间序列（MTS）分类在过去十年中变得越来越重要。目前最先进的MTS分类器是一种重量级深度学习方法，仅在大型数据集上优于第二好的MTS分类器。此外，这种深度学习方法不能提供忠实的解释，因为它依赖于事后模型不可知论的可解释性方法，这可能会阻碍其在许多应用中的使用。在本文中，我们提出了XCM，一个可解释的卷积神经网络用于MTS分类。XCM是一种新型的紧凑卷积神经网络，它直接从输入数据中提取与观测变量和时间相关的信息。因此，XCM体系结构在大型和小型数据集上都具有良好的泛化能力，同时通过精确识别对预测很重要的观察变量和输入数据的时间戳，允许充分利用忠实的特定于模型的事后可解释性方法（梯度加权类激活映射）。我们首先展示了XCM在大型和小型公共UEA数据集上都优于最先进的MTS分类器。然后，我们说明了XCM如何在合成数据集上协调性能和可解释性，并表明XCM能够更精确地识别输入数据的区域，这些区域对预测很重要，而当前的深度学习MTS分类器也提供了忠实的可解释性。最后，我们将介绍XCM如何在实际应用程序中超越当前最精确的最先进算法，同时通过提供忠实的、信息更丰富的解释来增强可解释性。
Multivariate Time Series (MTS) classification has gained importance over the past decade with the increase in the number of temporal datasets in multiple domains. The current state-of-the-art MTS classifier is a heavyweight deep learning approach, which outperforms the second-best MTS classifier only on large datasets. Moreover, this deep learning approach cannot provide faithful explanations as it relies on post hoc model-agnostic explainability methods, which could prevent its use in numerous applications. In this paper, we present XCM, an eXplainable Convolutional neural network for MTS 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 large and small 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. We first show that XCM outperforms the state-of-the-art MTS classifiers on both the large and small public UEA datasets. Then, we illustrate how XCM reconciles performance and explainability on a synthetic dataset and show that XCM enables a more precise identification of the regions of the input data that are important for predictions compared to the current deep learning MTS classifier also providing faithful explainability. Finally, 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.