Time Series Classification in Cyber-Physical System Applications by Intervals’ Numbers Techniques

Cyber-Physical Systems (CPSs) account for technical devices with both sensing and reasoning abilities. Our interest here is in classifying time series of sensory data by fuzzy logic in CPS applications. More specifically, a time series is represented by the distribution of its data samples using a novel Intervals' Number (IN) induction technique. We demonstrate the effectiveness of our techniques in a benchmark classification problem regarding ElectroEncephaloGraphy (EEG) signals. Mathematically sound extensions of the c-means as well as the kNN algorithms result in good classification performance comparatively to alternative methods from the literature. Potential future enhancements of the proposed techniques are discussed.