Time-line hidden Markov experts for time series prediction

Author

Wang, Xin ; Whigham, Peter ; Deng, Da ; Purvis, Martin

Author_Institution

Dept. of Inf. Sci., Otago Univ., Dunedin, New Zealand

Volume

1

fYear

2003

fDate

14-17 Dec. 2003

Firstpage

786

Abstract

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.

Keywords

divide and conquer methods; hidden Markov models; learning (artificial intelligence); neural nets; prediction theory; time series; mixture of experts algorithm; modularised connectionist model; state transition property; time series prediction; time-line hidden Markov experts; Data mining; Hidden Markov models; Information science; Markov processes; Noise measurement; Predictive models; State-space methods; Time measurement; Time series analysis; White noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on

Conference_Location

Nanjing

Print_ISBN

0-7803-7702-8

Type

conf

DOI

10.1109/ICNNSP.2003.1279393

Filename

1279393