Open AccessA hybrid prediction model of multivariate chaotic time series based on error correction
Author(s) -
Min Han,
Mingming Xu
Publication year - 2013
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.62.120510
Subject(s) - autoregressive model , series (stratigraphy) , computer science , chaotic , nonlinear system , echo state network , time series , multivariate statistics , algorithm , autoregressive–moving average model , nonlinear autoregressive exogenous model , mathematics , artificial intelligence , artificial neural network , recurrent neural network , statistics , machine learning , paleontology , physics , quantum mechanics , biology
Considering the problem that simply modifying the reservoir algorithm cannot significantly improve the prediction accuracy of chaotic multivariate time series, in this paper we propose a hybrid prediction model based on error correction. The observed data includes both linear and nonlinear features. First, we use autoregressive and moving average model to capture the linear features, then build a regularized echo state network to portray the dynamic nonlinear features. Finally, we add the predicted nonlinear value to the predicted linear value, in order to improve forecasting accuracy achieved by either of the models used separately. The experimental results of Lorenz and Sunspot-Runoff in the Yellow River time series demonstrate the effectiveness and characteristics of the proposed model herein.