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.