An Early Classification Approach for Multivariate Time Series of On-Vehicle Sensors in Transportation

Abstract

An important issue of research in the transportation system is timely classification of the inside-outside environment of the vehicle using sensors. The sensors generate the multivariate time series data, which requires a classification technique to classify it in real-time. Road surface classification is an example, where multivariate time series data can be used for early identification of the type of road surface. The challenge is to maintain the accuracy of the classification using a minimum number of data points of the multivariate time series. This work proposes an early classification approach for multivariate time series with a desired level of accuracy. It is assumed that the number of samples in the time series are not equal for a given period of time due to different type of sensors. Gaussian Process learning method is used to first estimate the minimum required length of the time series which helps to build an ensemble classifier with a desired level of accuracy. The ensemble classifier is used to predict the class label of an incoming multivariate time series. This work demonstrates a road surface classification system using the built ensemble classifier. Finally, the ensemble classifier is also evaluated on the various existing datasets from other domains. The results demonstrate the significance of early classification approach using accuracy, earliness, and confusion matrix, with the minimum required data points.