TimeSliver : 基于符号-线性分解的可解释时间序列分类
TimeSliver : Symbolic-Linear Decomposition for Explainable Time Series Classification
AI总结：
TimeSliver通过结合原始时间序列数据和符号抽象，提出了一种新的可解释性驱动的深度学习框架，有效提升时间序列分类的可解释性和预测性能。
摘要：
识别每个时间段对模型预测影响的范围对于解释模型决策和提高透明度至关重要。尽管基于梯度和特征属性的后验可解释方法已很流行，但它们存在参考状态敏感性，并且在时间序列数据集上难以泛化，因为它们将时间点独立处理并忽略序列依赖性。另一种可解释时间序列分类的方法是通过模型的可解释组件，例如利用自注意力机制来估计时间属性；然而，最近的研究表明，这些注意力权重往往无法提供忠实的时间重要性度量。在本工作中，我们扩展了这一视角，提出了一种新的可解释性驱动的深度学习框架TimeSliver，它联合使用原始时间序列数据及其符号抽象，构建一个保持原始时间结构的表示。该表示中的每个元素线性编码每个时间段对最终预测的贡献，使我们能够为每个时间点分配有意义的重要性分数。对于时间序列分类，TimeSliver在7个不同的合成和现实多变量时间序列数据集上比其他时间属性方法高出11%。TimeSliver还在26个UEA基准数据集上实现了与最先进基线模型相差不超过2%的预测性能，使其成为一种强大的、可解释的时间序列分类框架。
英文摘要：
Identifying the extent to which every temporal segment influences a model's predictions is essential for explaining model decisions and increasing transparency. While post-hoc explainable methods based on gradients and feature-based attributions have been popular, they suffer from reference state sensitivity and struggle to generalize across time-series datasets, as they treat time points independently and ignore sequential dependencies. Another perspective on explainable time-series classification is through interpretable components of the model, for instance, leveraging self-attention mechanisms to estimate temporal attribution; however, recent findings indicate that these attention weights often fail to provide faithful measures of temporal importance. In this work, we advance this perspective and present a novel explainability-driven deep learning framework, TimeSliver, which jointly utilizes raw time-series data and its symbolic abstraction to construct a representation that maintains the original temporal structure. Each element in this representation linearly encodes the contribution of each temporal segment to the final prediction, allowing us to assign a meaningful importance score to every time point. For time-series classification, TimeSliver outperforms other temporal attribution methods by 11% on 7 distinct synthetic and real-world multivariate time-series datasets. TimeSliver also achieves predictive performance within 2% of state-of-the-art baselines across 26 UEA benchmark datasets, positioning it as a strong and explainable framework for general time-series classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
