Classification of chaotic time series data based on the average length of close orbits
基于近邻轨道平均长度的混沌时间序列分类方法

Based on recurrence plot (RP) of chaotic time series, this paper develops the definition of cross recurrence plot (CRP) and the average length of close orbits, which reflects the similarity of two time series and is used to classify time series. By analysing the Lorenz signals, we conclude that the average length is decreased with the decrease of the similarity of two orbits. We have used our method and the cross-prediction error in the detection of ECG rhythm. The results show that both methods work for rhythm with different waveforms, but our method works better for rhythms with similar waveforms than the method of cross-prediction error.