A spiking neural network based on temporal encoding for electricity price time series forecasting in deregulated markets

In this paper a general methodology is proposed for development of spiking neural networks (SNN) as a time series modeling task. A continuous firing temporal encoding scheme is employed in the developed model for efficient handling of temporal correlations in high dimensional chaotic time series. The universal nonlinear function approximation property and unique ability of temporally encoded SNN is particularly advantageous in complex dynamics scenario. Rich dynamics of spiking neural networks are exploited for forecasting in electricity price time series system. The temporal encoding scheme proposed particularly for time series applications produced interesting results which encourage further research in this direction.

[1]  Ammar Belatreche,et al.  Advances in Design and Application of Spiking Neural Networks , 2006, Soft Comput..

[2]  Zhen Li,et al.  Research on Overcoming the Local Optimum of BPNN , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[3]  Thomas Natschläger,et al.  Pattern analysis with spiking neurons using delay coding , 1999, Neurocomputing.

[4]  Takashi Kanamaru,et al.  Blowout bifurcation and On-off intermittency in Pulse Neural Networks with Multiplec Modules , 2006, Int. J. Bifurc. Chaos.

[5]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[6]  Hans-Georg Beyer,et al.  The Theory of Evolution Strategies , 2001, Natural Computing Series.

[7]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[8]  Terence D. Sanger,et al.  Probability Density Methods for Smooth Function Approximation and Learning in Populations of Tuned Spiking Neurons , 1998, Neural Computation.

[9]  William Bialek,et al.  Reading a Neural Code , 1991, NIPS.

[10]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[11]  K. Bhattacharya,et al.  Forecasting the hourly Ontario energy price by multivariate adaptive regression splines , 2006, 2006 IEEE Power Engineering Society General Meeting.

[12]  Sander M. Bohte,et al.  Applications of spiking neural networks , 2005, Inf. Process. Lett..

[13]  Andrew D. Back,et al.  A spiking neural network architecture for nonlinear function approximation , 2001, Neural Networks.

[14]  Linda Bushnell,et al.  Fast Modifications of the SpikeProp Algorithm , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[15]  Simei Gomes Wysoski,et al.  Fast and adaptive network of spiking neurons for multi-view visual pattern recognition , 2008, Neurocomputing.

[16]  Qingxiang Wu,et al.  Evolutionary design of spiking neural networks. , 2006 .

[17]  A. Venturini,et al.  Day-ahead market price volatility analysis in deregulated electricity markets , 2002, IEEE Power Engineering Society Summer Meeting,.

[18]  Qingxiang Wu,et al.  Learning under weight constraints in networks of temporal encoding spiking neurons , 2006, Neurocomputing.

[19]  Wolfgang Maass,et al.  Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons , 1996, NIPS.

[20]  Wolfgang Maass,et al.  Fast Sigmoidal Networks via Spiking Neurons , 1997, Neural Computation.

[21]  KasabovNikola,et al.  Fast and adaptive network of spiking neurons for multi-view visual pattern recognition , 2008 .

[22]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[23]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[24]  Tomonobu Senjyu,et al.  A neural network based several-hour-ahead electric load forecasting using similar days approach , 2006 .

[25]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[26]  Blanca Cases,et al.  Topos: Spiking neural networks for temporal pattern recognition in complex real sounds , 2008, Neurocomputing.