Time-Line Hidden Markov Experts For Time Series Prediction
PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS & SIGNAL PROCESSING, PROCEEDINGS, VOLS 1 AND 2（2003）
摘要
A modularised connectionist model, based on the Mixture of Experts (ME) algorithm for time series prediction, is introduced. A group of connectionist modules learn to be local experts over some commonly appeared states in a time series. The dynamics for combining the experts is a hidden Markov process, in which the states of a time series are regarded as states of a HMM and each of them associates to an expert. However, the state transition property is time-variant and conditional on the dynamic situation of the time series.
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关键词
mixture of experts,expectation and maximisation,⎯time series prediction,hidden markov model,hidden markov models,time series prediction,learning artificial intelligence,state transition,time series,neural nets
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