On discovery and exploitation of temporal structure in data sets. / Scarfe, Tim.

2015. 175 p.

Research output: ThesisDoctoral Thesis




This thesis explores temporal structure based on self-similarity in different contexts.

An efficient dynamic programming algorithm is presented which discovers temporal structures in music shows, obtains high quality results, and compares them to similar algorithms used in the literature. The program segments a self-similarity matrix given a cost function and a fixed number of homogeneous
temporal structures to find. This is the initial approach we use to discover temporal structures in music data.

The use of a self-similarity matrix to visualize temporal structures is discussed in detail. Then the following question is explored; if similar temporal structures in other corpora existed; could forecasting algorithms be adapted to take advantage of them even if they were not known a priori?

Prediction with expert advice techniques are then introduced to exploit a priori unknown temporal structures of a similar configuration in an on-line configuration. Uni-variate Russian Stock Exchange options futures volatility corpora are used, which are highly interesting for on-line forecasting.

We experiment with merging together expert models which have been trained in some way to recognise temporal structures in corpora. The first types are kernel ridge regression models trained to be experts on particular regions in time, or
untrained and given random sets of parameters which may work well on certain time regions. The other types of model used are parsimonious predictors which transform uni-variate financial data into elementary time series based on homogeneous vicinities of information in the side domain. Expert merging techniques are then used across these time series which produce a validation-free forecaster comparable to sliding kernel ridge regression.
Original languageEnglish
Awarding Institution
Award date1 Apr 2015
StateUnpublished - 2015
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 24367943