Parallel distributed processing: explorations in the microstructure of cognition, vol. 2: psychological and biological models
Multilayer feedforward networks are universal approximators
Neural Networks
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An investigation of the use of feedforward neural networks for forecasting
An investigation of the use of feedforward neural networks for forecasting
A practical Bayesian framework for backpropagation networks
Neural Computation
Neural network models for time series forecasts
Management Science
Issues in Bayesian analysis of neural network models
Neural Computation
Bayesian radial basis functions of variable dimension
Neural Computation
An application of reversible-jump MCMC to multivariate spherical Gaussian mixtures
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Reversible Jump MCMC Simulated Annealing for Neural Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Robust Full Bayesian Learning for Radial Basis Networks
Neural Computation
Sequential Monte Carlo for model selection and estimation of neural networks
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
IEEE Transactions on Signal Processing
Predicting sun spots using a layered perceptron neural network
IEEE Transactions on Neural Networks
Random Ordinality Ensembles$\colon$ A Novel Ensemble Method for Multi-valued Categorical Data
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Optimization procedure for predicting nonlinear time series based on a non-Gaussian noise model
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
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In this article, we apply Bayesian neural networks (BNNs) to time series analysis, and propose a Monte Carlo algorithm for BNN training. In addition, we go a step further in BNN model selection by putting a prior on network connections instead of hidden units as done by other authors. This allows us to treat the selection of hidden units and the selection of input variables uniformly. The BNN model is compared to a number of competitors, such as the Box-Jenkins model, bilinear model, threshold autoregressive model, and traditional neural network model, on a number of popular and challenging data sets. Numerical results show that the BNN model has achieved a consistent improvement over the competitors in forecasting future values. Insights on how to improve the generalization ability of BNNs are revealed in many respects of our implementation, such as the selection of input variables, the specification of prior distributions, and the treatment of outliers.