Modelling of chaotic time series using a variable-length windowing approach

Abstract

Abstract A multidimensional feature extraction method for chaotic time series is presented. The method uses a variable-length windowing approach. Mackey–Glass delay-difference equation is used to create chaotic sample signals. Among many possible alternatives, the length of the data segment having the smallest variance fractal dimension (VFD) value is found and used as the feature value. Multidimensional feature vectors are formed to model each sample signal. A probabilistic neural network (PNN) is trained and tested with these vectors. It is shown that the application of the new feature extraction method improves the classification performance of the PNN as compared to a VFD based feature extraction method using a fix-length windowing approach.