Real time recurrent neural networks for time series prediction and confidence estimation

This paper explores two established techniques for doing time series modeling and prediction of mean and variance. The first method is an explicit method used to establish the embedding dimension of the time series, define the sensitivity of each variable and come up with a systematic decision of the delay of inputs for future prediction. The second method makes use of recurrent networks to implicitly derive models with "adaptive" time delays for the mean and variance predictions of a given time series. The recurrent system gives better prediction performance on artificial chaotic signals as well as real world exchange rate data in terms of mean squared error criterion and requires no laborious determination of the number of inputs.