Priors for time series forecasting

Regularization is a method for improving the solution of ill-posed problems with neural networks. In regularization, a penalty term, called regularizer or prior, is added to the performance function. The penalty term is weighted with a regularization parameter, 2, to balance the trade-off between model bias and model variance. We have compared the performances of different priors on several different time series data sets, to see if there is any consistent difference in performance between priors. The conclusions from our study on real world time series data has weight decay the best performance and the Bishop smoother is the worst choice.