Time-line hidden Markov experts for time series prediction

被引：0

作者：

Wang, X ^{[1
]}

Whigham, P ^{[1
]}

Deng, D ^{[1
]}

Purvis, M ^{[1
]}

机构：

[1] Univ Otago, Dept Informat Sci, Dunedin 9001, New Zealand

来源：

PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS & SIGNAL PROCESSING, PROCEEDINGS, VOLS 1 AND 2 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：786 / 789

页数：4

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