PREDICTIVE MINIMUM DESCRIPTION LENGTH CRITERION FOR TIME-SERIES MODELING WITH NEURAL NETWORKS

Nonlinear time series modeling with a multilayer perceptron network is presented. An important aspect of this modeling is the model selectio n, i.e., the problem of determining the size as well as the complexity of the model. To overcome this problem we apply the predictive minimu m description length (PMDL) principle as a minimization criterion. In the neural network scheme it means minimizing the number of input and hidden units. Three time series modeling experiments are used to exami ne the usefulness of the PMDL model selection scheme. A comparison wit h the widely used cross-validation technique is also presented. In our experiments the PMDL scheme and the cross-validation scheme yield sim ilar results in terms of model complexity. However, the PMDL method wa s found to be two times faster to compute. This is significant improve ment since model selection in general is very time consuming.