Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series

DOI10.1080/00207721.2014.955552zbMath1335.62132OpenAlexW1967150189MaRDI QIDQ2798518

Min Gan, C. L. Philip Chen, Long Chen, Chun-Yang Zhang

Publication date: 12 April 2016

Published in: International Journal of Systems Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207721.2014.955552

zbMATH Keywords

modellingtime seriesforecastingvarying coefficient modelstate-dependent model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (8)

Parameter identification of a nonlinear radial basis function‐based state‐dependent autoregressive network with autoregressive noise via the filtering technique and the multiinnovation theory ⋮ Fitting the exponential autoregressive model through recursive search ⋮ A variable projection method for the general radial basis function neural network ⋮ Kernel‐based regularization least squares algorithm for nonlinear time‐delayed systems using self‐organizing maps ⋮ Highly computationally efficient state filter based on the delta operator ⋮ A robust meta-heuristic adaptive Bi-CGSTAB algorithm to online estimation of a three DoF state–space model in the presence of disturbance and uncertainty ⋮ Modeling and Control Approach to Coupled Tanks Liquid Level System Based on Function‐Type Weight RBF‐ARX Model ⋮ Parameter estimation for an exponential autoregressive time series model by the Newton search and multi-innovation theory

Cites Work

• Unnamed Item
• Time series forecasting using a hybrid ARIMA and neural network model
• Multivariable RBF-ARX model-based robust MPC approach and application to thermal power plant
• Statistical methods with varying coefficient models
• Comparison study of AR models on the Canadian lynx data: A close look at BDS statistic
• Forecasting nonlinear time series with a hybrid methodology
• Functional Coefficient Autoregressive Models: Estimation and Tests of Hypotheses
• A Quasi-ARMAX approach to modelling of non-linear systems
• Non-linear time series models for non-linear random vibrations
• Modelling nonlinear random vibrations using an amplitude-dependent autoregressive time series model
• STATE-DEPENDENT MODELS: A GENERAL APPROACH TO NON-LINEAR TIME SERIES ANALYSIS
• Stochastic Neural Networks With Applications to Nonlinear Time Series
• Nonlinear time series modelling with the radial basis function-based state-dependent autoregressive model
• Local Global Neural Networks
• The wavelet-NARMAX representation: A hybrid model structure combining polynomial models with multiresolution wavelet decompositions
• Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial Spline Approach
• A new look at the statistical model identification

This page was built for publication: Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series