Interpretable classification of time-series data using efficient enumerative techniques
Interpretable classification of time-series data using efficient enumerative techniques
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DOI：
10.1145/3365365.3382218
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发表时间：
2019-07
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影响因子：
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通讯作者：
Sara Mohammadinejad;Jyotirmoy V. Deshmukh;Aniruddh Gopinath Puranic;Marcell Vazquez-Chanlatte;Alexandre Donzé
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作者：
Sara Mohammadinejad;Jyotirmoy V. Deshmukh;Aniruddh Gopinath Puranic;Marcell Vazquez-Chanlatte;Alexandre Donzé
自动驾驶汽车、可穿戴设备和航空电子系统等网络物理系统应用产生了大量的时间序列数据。设计人员经常寻找工具来帮助对数据进行分类和分类。针对时间序列数据的传统机器学习技术为解决这些问题提供了多种解决方案；然而，这些算法训练出来的伪影往往缺乏可解释性。另一方面，时态逻辑，如信号时态逻辑(STL)，已经被成功地用于形式化方法界，作为对时间序列行为的规范。在这项工作中，我们提出了一种新的技术来自动学习能够对实值时间序列数据进行分类的时态逻辑公式。以前关于从数据中学习STL公式的工作要么假设由用户给出公式模板，要么假设能够以系统的方式探索公式结构的STL的一些特殊片段。在我们的技术中，我们放松了这些假设，并提供了一种系统地探索所有STL公式空间的方法。由于所有STL公式的空间非常大，并且包含许多语义等价的公式，我们提出了一种启发式剪枝技术来剪除所考虑的公式空间。最后，我们通过汽车和交通领域的各种案例研究来说明我们的技术。
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data. Designers often look for tools to help classify and categorize the data. Traditional machine learning techniques for time-series data offer several solutions to solve these problems; however, the artifacts trained by these algorithms often lack interpretability. On the other hand, temporal logic, such as Signal Temporal Logic (STL) have been successfully used in the formal methods community as specifications of time-series behaviors. In this work, we propose a new technique to automatically learn temporal logic formulas that are able to classify real-valued time-series data. Previous work on learning STL formulas from data either assumes a formula-template to be given by the user, or assumes some special fragment of STL that enables exploring the formula structure in a systematic fashion. In our technique, we relax these assumptions, and provide a way to systematically explore the space of all STL formulas. As the space of all STL formulas is very large, and contains many semantically equivalent formulas, we suggest a technique to heuristically prune the space of formulas considered. Finally, we illustrate our technique on various case studies from the automotive and transportation domains.