Time-line hidden Markov experts and its application in time series prediction
Wang, Xin; Whigham, Peter A; Deng, Da

View/ Open
Cite this item:
Wang, X., Whigham, P. A., & Deng, D. (2003). Time-line hidden Markov experts and its application in time series prediction (Information Science Discussion Papers Series No. 2003/03). Information Science. Retrieved from http://hdl.handle.net/10523/1097
Permanent link to OUR Archive version:
http://hdl.handle.net/10523/1097
Abstract:
A modularised connectionist model, based on the Mixture of Experts (ME) algorithm for time series prediction, is introduced. A set of connectionist modules learn to be local experts over some commonly appearing states of a time series. The dynamics for mixing the experts is a Markov process, in which the states of a time series are regarded as states of a HMM. Hence, there is a Markov chain along the time series and each state associates to a local expert. The state transition on the Markov chain is the process of activating a different local expert or activating some of them simultaneously by different probabilities generated from the HMM. The state transition property in the HMM is designed to be time-variant and conditional on the first order dynamics of the time series. A modified Baum–Welch algorithm is introduced for the training of the time-variant HMM and it has been proved that by EM process the likelihood function will converge to a local minimum. Experiments, with two time series, show this approach achieves significant improvement in the generalisation performance over global models.
Date:
2003-06
Publisher:
Information Science
Pages:
21
Series number:
2003/03
Keywords:
time series prediction; Mixture of Experts; HMM; connectionist model; expectation and maximization; Gauss probability density distribution
Research Type:
Discussion Paper
Collections
- Information Science [486]
- Discussion Paper [438]