An artificial neural network based approach for power spectrum estimation and simulation of stochastic processes subject to missing data

An artificial neural network (ANN) approach is presented as a possible solution to overcoming the problems associated with missing data in spectral analysis and/or simulation of stochastic processes. By using an ANN to capture patterns present in the available data, gaps can then be filled or entirely new processes generated. A feed-forward ANN is used with ordered inputs and Gaussian white noise to represent missing data during learning. The solution is broadly applicable in many circumstances due to the fact that it assumes no prior knowledge of the underlying statistics of the process. Specifically, to present the method in context, this paper addresses some of the challenges associated with preparing data for environmental simulation load models (time dependent, 1-dimensional).