1.
Chaos
; 23(4): 043104, 2013 Dec.
Artículo
en Inglés
| MEDLINE
| ID: mdl-24387543
RESUMEN
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.