Slope Entropy Characterisation: Adding Another Interval Parameter to the Original Method

Abstract

Slope Entropy (SlpEn) is a recently proposed time series entropy estimation method for classification. This method has yielded better results than other similar methods in all the published studies so far. It is based on a signal-gradient thresholding scheme using two parameters, δ and γ, in addition to the usual embedded dimension parameter m. In this work, we investigated the possibility of adding one thresholding parameter more, termed θ, and we compared the original method to the new one. The experiment results showed a small improvement using the new method in terms of classification accuracy. However, the temporal cost increased significantly and therefore we concluded it is not worth the extra effort unless maximum accuracy is of utmost importance.

Keywords

• slope entropy
• time series classification
• parameter optimisation