Local model optimization for time series prediction.
James McNames · The European Symposium on Artificial Neural Networks · 2000
Local models have emerged as one of the leading methods of chaotic time series prediction. However, the accuracy of local models is sensitive to the choice of user-specified parameters, not unlike neural networks and other methods. This paper describes a method of optimiz- ing these parameters so as to minimize the leave-one-out cross-validation error. This approach reduces the burden on the user to pick appropriate values and improves the prediction accuracy.