Neural Network Prediction of Non-Linear Time Series Using Predictive MDL Principle
- 25 August 2005
- proceedings article
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
A neural network approach for modelling and predictions on non-linear time series is presented. The main aim is to reduce the size and complexity of the network and use the least number of weights and nodes for any predictive mapping. The problem of selecting the number of input and hidden nodes is studied by the predictive minimum description length principle. We discuss comparatively the performance of neural networks and conventional methods in predicting non linear time series. The neural network is found to yield better predictions than an optimum ARMA model.Keywords
This publication has 13 references indexed in Scilit:
- Approximation by superpositions of a sigmoidal functionMathematics of Control, Signals, and Systems, 1989
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989
- Stochastic complexity and the mdl principleEconometric Reviews, 1987
- 6 Various model selection techniques in time series analysisPublished by Elsevier ,1985
- A Universal Prior for Integers and Estimation by Minimum Description LengthThe Annals of Statistics, 1983
- The determination of optimum structures for the state space representation of multivariate stochastic processesIEEE Transactions on Automatic Control, 1982
- Inconsistency of the AIC rule for estimating the order of autoregressive modelsIEEE Transactions on Automatic Control, 1980
- Modeling by shortest data descriptionAutomatica, 1978
- Estimating the Dimension of a ModelThe Annals of Statistics, 1978
- A new look at the statistical model identificationIEEE Transactions on Automatic Control, 1974