Multivariate time series data are ubiquitous in the application of machine learning to problems in the physical sciences. Chemiresistive sensor arrays are highly promising in chemical detection tasks relevant to industrial, safety, and military applications. Sensor arrays are an inherently multivariate time series data collection tool which demand rapid and accurate classification of arbitrary chemical analytes. Previous research has benchmarked data-agnostic multivariate time series classifiers across diverse multivariate time series supervised tasks in order to find general-purpose classification algorithms. To our knowledge, there has yet to be an effort to survey machine learning and time series classification approaches to chemiresistive hardware sensor arrays for the detection of chemical analytes. In addition to benchmarking existing approaches to multivariate time series classifiers, we incorporate findings from a model survey to propose the novel \textit{ChemTime} approach to sensor array classification for chemical sensing. We design experiments addressing the unique challenges of hardware sensor arrays classification including the rapid classification ability of classifiers and minimization of inference time while maintaining performance for deployed lightweight hardware sensing devices. We find that \textit{ChemTime} is uniquely positioned for the chemical sensing task by combining rapid and early classification of time series with beneficial inference and high accuracy.

翻译：多变量时间序列数据在机器学习应用于物理科学问题时无处不在。化学电阻传感器阵列在工业、安全和军事相关的化学检测任务中具有巨大潜力。传感器阵列作为一种固有的多变量时间序列数据采集工具，需要对任意化学分析物进行快速准确的分类。此前研究已对各种多变量时间序列监督任务中的数据无关型多变量时间序列分类器进行了基准测试，以寻找通用分类算法。据我们所知，目前尚未有系统综述机器学习与时间序列分类方法在化学电阻硬件传感器阵列分析物检测中的应用研究。除了对现有方法进行基准测试外，我们通过模型调研提出了一种新颖的传感器阵列分类方法——ChemTime。针对硬件传感器阵列分类的独特挑战，我们设计了实验，重点考察分类器的快速分类能力以及在保持性能的同时最小化轻量级部署硬件传感设备推理时间的要求。实验表明，ChemTime通过结合时间序列的快速早期分类、高效推理与高精度特点，在化学传感任务中展现出独特优势。