用时序逻辑让时间序列分类结果可解释
Towards Interpretable Concept Learning over Time Series via Temporal Logic Semantics
- 将时间序列直接嵌入时序逻辑公式空间，实现分类与解释统一
- 在多个数据集上达到与黑盒模型相当的准确率
- 适合需要可解释决策的安全关键领域应用
时间序列分类在安全关键场景中至关重要，但传统深度学习方法多为黑箱，难以解释。本文提出一种神经符号框架，通过引入受时序逻辑（STL）启发的核函数，将原始时间序列映射到预定义STL公式的匹配程度空间，实现分类与解释的联合优化。每个预测均附带最相关的逻辑概念作为解释，使分类基于人类可理解的时序模式，并生成局部与全局的符号化解释。初步实验表明，该方法在多个数据集上表现优异，同时提供高质量的逻辑推理依据。
原文摘要 · Abstract (English)
Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box deep learning methods, making it hard for humans to understand the rationale behind their output. To take on this challenge, we propose a neuro-symbolic framework that unifies classification and explanation through direct embedding of trajectories into a space of Signal Temporal Logic (STL) concepts. By introducing a novel STL-inspired kernel that maps raw time series to their alignment with predefined STL formulae, our model jointly optimises for accuracy and interpretability, as each prediction is accompanied by the most relevant logical concepts that characterise it. This enables classification grounded in human-interpretable temporal patterns and produces both local and global symbolic explanations. Early results show competitive performance while offering high-quality logical justifications for model decisions.
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