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A RBF neural network model with GARCH errors: Application to electricity price forecasting by Leandro dos Santos Coelho; André A.P. Santos is a Engineering article available to read on EtoBox.
What is A RBF neural network model with GARCH errors: Application to electricity price forecasting about?
In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose an estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide multi-step-ahead point and direction-of-change forecasts of the Spanish electricity pool prices.
Who reads A RBF neural network model with GARCH errors: Application to electricity price forecasting?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Leandro dos Santos Coelho; André A.P. Santos
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0378-7796)
- Published
- 2011
- Language
- EN
- Field
- Engineering (Physical Sciences)