Seng Neo, Kian and Leithead, W.E. and Zhang, Yunong (2006) Multi-frequency scale Gaussian regression for noisy time-series data. In: International Control Conference (ICC2006) , 30th August to 1st September 2006, Glasgow, Scotland, United Kingdom .

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Abstract

Regression using Gaussian process models is applied to time-series data analysis. To extract from the data separate components with different frequency scales, the Gaussian regression methodology is extended through the use of multiple Gaussian process models. Fast and memory-efficient methods, as required by Gaussian regression to cater for large time-series data sets, are discussed. These methods are based on the generalised Schur algorithm and a procedure to determine the Schur decomposition of matrices, the key step to realising them, is presented. In addition, a procedure to appropriately initialise the Gaussian process model training is presented. The utility of the procedures is illustrated by application of a multiple Gaussian process model to extract separate components with different frequency scales from a 5000-point time-series data set with gaps.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Gaussian regression; Multi-length scale; Time-series analysis; Missing data; Generalised Schur algorithm; ICC2006; Hamilton Institute.
Academic Unit:	Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Science and Engineering > Mathematics and Statistics
Item ID:	1776
Depositing User:	Hamilton Editor
Date Deposited:	11 Jan 2010 16:34
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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