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Structure parameter optimized kernel based online prediction with a generalized optimization strategy for nonstationary time series

Structure parameter optimized kernel based online prediction with a generalized optimization strategy for nonstationary time series
Structure parameter optimized kernel based online prediction with a generalized optimization strategy for nonstationary time series
In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction algorithms as usual consist of the selection of kernel structure parameters and the kernel weight vector updating. For structure parameters, the kernel dictionary is selected by sparsification techniques with selective online modeling criteria, and the symmetric kernel covariance matrix is intermittently optimized with the covariance matrix adaptation evolution strategy (CMA-ES). This intermittent optimization can not only improve the kernel structure's flexibility by utilizing the cross relatedness of input variables, but also partly alleviate the prediction uncertainty arisen by the kernel dictionary selection for nonstationary time series. In order to sufficiently capture the underlying dynamic characteristics in prediction-error time series, a generalized optimization strategy is designed to sequentially construct the kernel dictionary selection and weight vector updating procedures in multiple kernel connection modes. The generalized optimization strategy is highly flexible and effective, and it is capable of enhancing the ability to adaptively track the changing dynamic characteristics due to nonstationarity. Finally, in the perspective of top-level design, we summarize the information interaction between the network topology in kernel regressors and the optimization of inner model parameters. Numerical simulations demonstrate that the proposed approach has superior prediction performance for nonstationary time series.
Covariance matrix adaptation evolution strategy, kernel adaptive filter algorithm, nonstationary time series, online prediction, prediction-error time series, radial basis function neural network
1053-587X
2698-2712
Guo, Jinhua
d8303b6d-8f47-419c-ba18-f43fe78cb884
Chen, Hao
f3be1b7f-86d4-418a-8e33-1c0f7ae2fb20
Zhang, Jingxin
30a8c6c1-ff47-431a-97b2-c679516aaffa
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Guo, Jinhua
d8303b6d-8f47-419c-ba18-f43fe78cb884
Chen, Hao
f3be1b7f-86d4-418a-8e33-1c0f7ae2fb20
Zhang, Jingxin
30a8c6c1-ff47-431a-97b2-c679516aaffa
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Guo, Jinhua, Chen, Hao, Zhang, Jingxin and Chen, Sheng (2022) Structure parameter optimized kernel based online prediction with a generalized optimization strategy for nonstationary time series. IEEE Transactions on Signal Processing, 70, 2698-2712, [3175014]. (doi:10.1109/TSP.2022.3175014).

Record type: Article

Abstract

In this paper, sparsification techniques aided online prediction algorithms in a reproducing kernel Hilbert space are studied for nonstationary time series. The online prediction algorithms as usual consist of the selection of kernel structure parameters and the kernel weight vector updating. For structure parameters, the kernel dictionary is selected by sparsification techniques with selective online modeling criteria, and the symmetric kernel covariance matrix is intermittently optimized with the covariance matrix adaptation evolution strategy (CMA-ES). This intermittent optimization can not only improve the kernel structure's flexibility by utilizing the cross relatedness of input variables, but also partly alleviate the prediction uncertainty arisen by the kernel dictionary selection for nonstationary time series. In order to sufficiently capture the underlying dynamic characteristics in prediction-error time series, a generalized optimization strategy is designed to sequentially construct the kernel dictionary selection and weight vector updating procedures in multiple kernel connection modes. The generalized optimization strategy is highly flexible and effective, and it is capable of enhancing the ability to adaptively track the changing dynamic characteristics due to nonstationarity. Finally, in the perspective of top-level design, we summarize the information interaction between the network topology in kernel regressors and the optimization of inner model parameters. Numerical simulations demonstrate that the proposed approach has superior prediction performance for nonstationary time series.

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TSP2022-June7 - Author's Original
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Structure_Parameter_Optimized_Kernel_Based_Online_Prediction_with_a_Generalized_Optimization_Strategy_for_Nonstationary_Time_Series - Accepted Manuscript
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Accepted/In Press date: 3 May 2022
e-pub ahead of print date: 13 May 2022
Published date: 7 June 2022
Keywords: Covariance matrix adaptation evolution strategy, kernel adaptive filter algorithm, nonstationary time series, online prediction, prediction-error time series, radial basis function neural network

Identifiers

Local EPrints ID: 457822
URI: http://eprints.soton.ac.uk/id/eprint/457822
ISSN: 1053-587X
PURE UUID: ed1ac8bd-701c-4fc6-85a6-7b6294a4c640

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Date deposited: 20 Jun 2022 16:36
Last modified: 10 Aug 2022 17:30

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Contributors

Author: Jinhua Guo
Author: Hao Chen
Author: Jingxin Zhang
Author: Sheng Chen

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