标题： 基于ECLAD-ts的时间序列概念提取 显示英文标题

标题： Concept Extraction for Time Series with ECLAD-ts

摘要： 时间序列分类（TSC）的卷积神经网络（CNNs）在从质量预测到医学诊断的应用中正得到越来越多的使用。 这些模型的黑箱性质使得理解其预测过程变得困难。 这个问题至关重要，因为CNNs容易学习捷径和偏见，这会损害它们的鲁棒性并违背人类预期。 为了评估是否使用了此类机制以及相关风险，必须提供反映模型内部工作原理的模型解释。 概念提取（CE）方法提供了这样的解释，但迄今为止主要是在图像领域开发的，因此在时间序列领域存在空白。 在这项工作中，我们基于图像领域CE方法的思想，提出了一种针对时间序列领域的CE和定位方法。 我们提出了新颖的方法ECLAD-ts，该方法根据模型在不同抽象层次上对输入子集的编码方式，提供事后全局解释。 为此，通过聚类CNN激活图的时间步聚合来生成概念，并根据其对预测过程的影响来计算其重要性。 我们在合成数据集和自然数据集上评估了我们的方法。 此外，我们通过实证结果评估了时间序列领域CE的优势和局限性。 我们的结果显示，ECLAD-ts有效利用模型的内部表示来解释模型，提供了有关其预测过程的有用见解。 显示英文摘要

摘要： Convolutional neural networks (CNNs) for time series classification (TSC) are being increasingly used in applications ranging from quality prediction to medical diagnosis. The black box nature of these models makes understanding their prediction process difficult. This issue is crucial because CNNs are prone to learning shortcuts and biases, compromising their robustness and alignment with human expectations. To assess whether such mechanisms are being used and the associated risk, it is essential to provide model explanations that reflect the inner workings of the model. Concept Extraction (CE) methods offer such explanations, but have mostly been developed for the image domain so far, leaving a gap in the time series domain. In this work, we present a CE and localization method tailored to the time series domain, based on the ideas of CE methods for images. We propose the novel method ECLAD-ts, which provides post-hoc global explanations based on how the models encode subsets of the input at different levels of abstraction. For this, concepts are produced by clustering timestep-wise aggregations of CNN activation maps, and their importance is computed based on their impact on the prediction process. We evaluate our method on synthetic and natural datasets. Furthermore, we assess the advantages and limitations of CE in time series through empirical results. Our results show that ECLAD-ts effectively explains models by leveraging their internal representations, providing useful insights about their prediction process.