Plastic network for predicting the Mackey-Glass time series

Abstract

A novel plastic network is introduced as a tool for predicting chaotic time series. When the goal is prediction accuracy for chaotic time series, local-in-time and local-in-state-space plastic networks can outperform the traditional global methods. The key ingredient of a plastic network is a model selection criterion that allows it to self organize by choosing among a collection of candidate models. Among the advantages of the plastic network for the prediction of (chaotic) time series are the simplicity of the models used, accuracy, relatively small data requirement, online usage, and ease of understanding of the algorithms. When reporting prediction results on chaotic time series, a careful analysis of the data is recommended. Specifically for the Mackey-Glass time series, the authors find that different forward lead size can result in different prediction accuracy.<>