Modelling and Prediction of Cyclostationary Chaotic Time Series Using Vector Autoregressive Models

It has been shown that some chaotic time series has cyclostationary characteristic. In this paper, this characteristic is exploited for applications to modeling and prediction of chaotic time series. To this aim, a vector-autoregressive-model-based model is developed. The model first transforms the scalar chaotic time series into a vector time series based on polyphase decomposition of cyclostationary time series, and then uses the vector autoregressive model for modeling and prediction purposes. The application of the proposed model to simulated data from the periodically perturbed logistic map is carried out and the results show that the model works well for modeling and long-term prediction in comparison with other models