Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Neural networks for pattern recognition
Neural networks for pattern recognition
Mixtures of Autoregressive Models for Financial Risk Analysis
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Machine Learning: From Theory to Applications - Cooperative Research at Siemens and MIT
Adaptive mixtures of local experts
Neural Computation
Hierarchical mixtures of autoregressive models for time-series modeling
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
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We compare the performance of competitive and collaborative strategies for mixtures of autoregressive experts with normal innovations for conditional risk analysis in financial time series. The prediction of the mixture of collaborating experts is an average of the outputs of the experts. If a competitive strategy is used the prediction is generated by a single expert. The expert that becomes activated is selected either deterministically (hard competition) or at random, with a certain probability (soft competition). The different strategies are compared in a sliding window experiment for the time series of log-returns of the Spanish stock index IBEX 35, which is preprocessed to account for the heteroskedasticity of the series. Experiments indicate that the best performance for risk analysis is obtained by mixtures with soft competition, where the experts have a probability of activation given by the output of a gating network of softmax units.