Chaotic time series forecast based on weighted state transition network

There have been many attempts to improve how well chaotic time-series can be used to make predictions. Predicting a chaotic time series beyond the short time limit is extremely difficult as chaotic trajectories diverge exponentially. Various approaches, including machine learning techniques, have been attempted in the literature to attain better prediction accuracy. A complex network has recently been suggested as a way to look at how a certain time series changes over time. Here, we propose a technique for forecasting time series based on an induced weighted state transition network. We demonstrate that the suggested approach can successfully predict chaotic time series up to a reasonable time limit. The proposed method is simple and computationally efficient.