Phase Space Reconstruction-Based Conceptor Network for Time Series Prediction

The Conceptor network is a newly proposed reservoir computing (RC) model, which outperforms traditional classifiers, which can fail to model new classes of data for a supervised learning task. However, the reservoir structure design for the Conceptor is single, involving just a traditional random network, which has strong coupling between nodes and limits computing ability. This study focused on the reservoir topology design problem, and we propose a complex network Conceptor-based phase space reconstruction of time series. Several dynamical systems were chosen to build complex networks using a phase space reconstruction algorithm. The experiment results obtained using a mix of two irrational-period sines showed that the proposed phase space reconstruction reservoir topologies with the appropriate values of threshold provide Conceptors with extra reconstruction precision. Among them, the phase space reconstruction reservoir-based Lorenz system shows the best performance. Further experiments also identified the appropriate values of threshold of the phase space reconstruction method required to obtain optimal performance. The precision showed a non-linear decline with increase in memory load, and the proposed Lorenz phase space reconstruction reservoir maintained its advantages under different memory loads.