A new and better prediction model for chaotic time series based on ESN and PCA

The introduction of the full paper points out that the prediction model based on ESN (echo state network) proposed by H. Jaeger in Ref. 1 is, in our opinion, not good enough. So we propose what we believe to be a new and better prediction model based on ESN and PCA (principal component analysis). Section 1 briefs the relevant information in Refs. 2 through 6. Section 2 explains our prediction model; its block diagram is given in Fig. 2. Section 3 gives eq. (6) for measuring the prediction precision of our prediction model. Section 4 compares the simulation results of our prediction model with those of the prediction model based on ESN. The simulation results, given in Figs. 4 through 7 and in Tables 1, 2 and 3, and their analysis show preliminarily that: (1) the computing complexity of our prediction model is lower; (2) its training time is shorter; (3)the prediction rate is higher than that of the prediction model based on ESN.