title: 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.
authors.affiliation: Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Paucar Bravo
authors.affiliation: Graduate School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
authors.forename: Kazuyuki
authors.initials: K
authors.lastname: Aihara
authors.affiliation: Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
authors.forename: Yoshito
authors.initials: Y
authors.lastname: Hirata
mesh_terms: D008962:Models, Theoretical