Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of Autoregressive Models for Financial Risk Analysis
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Adaptive mixtures of local experts
Neural Computation
Risk-neutral density extraction from option prices: improved pricing with mixture density networks
IEEE Transactions on Neural Networks
Competitive and collaborative mixtures of experts for financial risk analysis
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
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A hierarchical mixture of autoregressive (AR) models is proposed for the analysis of nonlinear time-series. The model is a decision tree with soft sigmoidal splits at the inner nodes and linear autoregressive models at the leaves. The global prediction of the mixture is a weighted average of the partial predictions from each of the AR models. The weights in this average are computed by the application of the hierarchy of soft splits at the inner nodes of the tree on the input, which consists in the vector of the delayed values of the time series. The weights can be interpreted as a priori probabilities that an example is generated by the AR model at that leaf. As an illustration of the flexibility and robustness of the models generated by these mixtures, an application to the analysis of a financial time-series is presented.