The Generative Topographic Mapping is a probability density model which describes the distribution of data in a space of several dimensions in terms of a smaller number of latent (or hidden) variables. The standard GTM (generative topographic mapping) has been extended to model time series by incorporating it as the emission density in a hidden Markov model. This thesis studies the use of the Generative Topographic Mapping through time model for predicting regime shifts in financial market data. We looked at several aspects of the model, and trained it on different data sets and show the process of quantifying the information in the visualisation plot.
Date of Award | Sept 2002 |
---|
Original language | English |
---|
Awarding Institution | |
---|
Supervisor | Ian Nabney (Supervisor) |
---|
Temporal Visualisation
Ballagan, M. (Author). Sept 2002
Student thesis: Master's Thesis › Master of Science (by Research)