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Early Classification Approach for Multivariate Time Series using Sensors of Different Sampling Rate

Classification of Multivariate Time Series (MTS) data has been an important area of research for many years. In time-critical applications, such as health informatics, fire detection, and disaster forecasting, it is desirable to classify the MTS data as early as possible. This work proposes an early classification approach to classify an incoming MTS. The early classification approach helps to predict the class label of an incoming MTS without waiting for the full length. Different from the existing work, this work considers that sampling rate of the sensors which generated the MTS is different. The performance of the approach is evaluated on a publicly available dataset using accuracy, earliness and energy consumption.