Weakly connected neural networks
Weakly connected neural networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks
Neural Computing and Applications
Confidence estimation methods for neural networks: a practical comparison
IEEE Transactions on Neural Networks
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This paper examines electricity price time series from dynamical system perspective and proposes a hybrid model which employs a synergistic combination of Recurrent Neural Network (RNN) and coupled excitable system for prediction of future prices in deregulated electricity markets. Driven by profit maximizing decisions taken by various agents, these markets belong to the class of financial systems. However presence of intermittent spikes and complex dynamic nonlinearities in electricity price time series render the prediction task extremely challenging. The approximation ability of Recurrent Neural Networks to map dynamic functions together with sharp jumping attribute of coupled excitable systems allows close approximation of spiky time series. The developed hybrid model was applied for point and interval forecasting in various markets worldwide over different seasons for testing its adaptability in different environments. Satisfactory prediction results were obtained in all the markets, in stable as well as spiking regions of the time series.