ChemTime: Rapid and Early Classification for Multivariate Time Series
  Classification of Chemical Sensors
Alexander M. Moore Randy C. Paffenroth Kenneth T. Ngo Joshua R. Uzarski
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.