## Speaker:

Xin Wang,
Department of Information Science

## Title:

"Time-line" Hidden Markov Experts for Time Series
Prediction

## Location:

Archway 2

## Abstract:

A modularised model, based on the Mixture of
Experts (ME) algorithm for time series prediction, will be introduced
in the talk. A set of connectionist models learn to be local experts
over some phases of the trajectory in the state space, so the
trajectory-regression task is divided into some simple ones and precise
approximation is expected.

The dynamics for combining the local experts is a Hidden Markov Model
(HMM), where the phases of the trajectory are regarded as the states of
a HMM and the state transition on the Markov chain is the process of
the underlying system evolving from one phase to another on the
trajectory.

The transition probabilities of the hidden Markov model (HMM) are
designed to be time-varying and a modified Baum-Welch algorithm is
introduced for the learning of the "time-line" HMM.

Last modified: Friday, 16-Jul-2004 16:31:53 NZST

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