CISE Postdoctoral Program: Robust Forecasting with Neural Networks

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9503968 Moody In this project robust artificial neural network (ANN) learning algorithms and tools for forecasting time series, with specific applications to financial and economic time series will be developed. These algorithms and tools are difficult to predict due to the non-normal distribution of the noise and the presence of outliers, as well as the nonstationarity of both noise and signals. They thus make excellent benchmark problems for robust nonlinear modeling techniques. In robust models, robust error measures for gracefully handling outliers in the data will be incorporated. Also, a new method for automatically regularizing (constraining) the ANN model during training by using second order statistics of the cost function gradient. This prevents the network from adapting to noise and thus produces robust models. In the area of learning algorithms, the application of a new simulated annealing type learning algorithm to recurrent networks will be investigated. It is believed that, based upon past experience with feedforward networks with many hidden layers, this algorithm will improve the performance of recurrent networks. In variable selection, a novel use of genetic algorithms (GA) in combination with ANN models to select optimal subsets of variables for solving a problem will be pursued. Combining GAs with a newly developed statistical test, the so-called delta-test, into a more powerful tool for determining the optimal variable subset will also be done. ***