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Multivariate time series data are ubiquitous in the application of machinelearning to problems in the physical sciences. Chemiresistive sensor arrays arehighly promising in chemical detection tasks relevant to industrial, safety,and military applications. Sensor arrays are an inherently multivariate timeseries data collection tool which demand rapid and accurate classification ofarbitrary chemical analytes. Previous research has benchmarked data-agnosticmultivariate time series classifiers across diverse multivariate time seriessupervised tasks in order to find general-purpose classification algorithms. Toour knowledge, there has yet to be an effort to survey machine learning andtime series classification approaches to chemiresistive hardware sensor arraysfor the detection of chemical analytes. In addition to benchmarking existingapproaches to multivariate time series classifiers, we incorporate findingsfrom a model survey to propose the novel \textit{ChemTime} approach to sensorarray classification for chemical sensing. We design experiments addressing theunique challenges of hardware sensor arrays classification including the rapidclassification ability of classifiers and minimization of inference time whilemaintaining performance for deployed lightweight hardware sensing devices. Wefind that \textit{ChemTime} is uniquely positioned for the chemical sensingtask by combining rapid and early classification of time series with beneficialinference and high accuracy.

ChemTime: Rapid and Early Classification for Multivariate Time Series Classification of Chemical Sensors