Chaotic time series classification by means of reservoir-based convolutional neural networks

We propose a novel Reservoir Computing (RC) based classification method that distinguishes between different chaotic time series. Our method is composed of two steps: (i) we use the reservoir as a feature extracting machine that captures the salient features of time series data; (ii) the readout layer of the reservoir is subsequently fed into a Convolutional Neural Network (CNN) to facilitate classification and recognition. One of the notable advantages is that the readout layer, as obtained by randomly generated empirical hyper-parameters within the RC module, provides sufficient information for the CNN to accomplish the classification tasks effectively. The quality of extracted features by RC is independently evaluated by the root mean square error, which measures how well the training signal may be reconstructed from the input time series. Furthermore, we propose two ways to implement the RC module, namely, a single shallow RC and parallel RC configurations, to further improve the classification accuracy. The important roles of RC in feature extraction are demonstrated by comparing the results when the CNN is provided with either ordinal pattern probability features or unprocessed raw time series directly, both of which perform worse than RC-based method. In addition to CNN, we show that the readout of RC is good for other classification tools as well. The successful classification of electroencephalogram recordings of different brain states suggests that our RC-based classification tools can be used for experimental studies.