Application of joint permutations for predicting coupled time series

Abstract

In this work, we introduce a model for predicting multivariate time series data. This model was obtained by partitioning the state space with joint permutations. We review the theoretical framework of the previous works, show a simple extension to multivariate data, and compare its performance to the previous model obtained by permutations for predicting scalar time series data. © 2013 AIP Publishing LLC.