Aykroyd, RG orcid.org/0000-0003-3700-0816 and Alfaer, N (2016) Sequential models for time-evolving regression problems with an application to energy demand prediction. Stochastic Modelling and Applications, 20 (1). pp. 1-16. ISSN 0972-3641
Abstract
In recent years, there has been a dramatic increase in the use of data that are collected over time and hence models with a temporal component, leading to dynamic models, have received increasing attention. The proposed approach uses a general framework which permits many special cases to be considered. Put simply, for each time a parametric observation model is defined with a conditional auto-regressive type model defined relating the parameters at one time to previous parameter values, this is called the evolution equation.
Simulation results will be presented investigating estimator properties considering a temporally changing regression problem with results demonstrating improved estimation. The technique will also be applied to a real dataset examining the changing relationship between ambient temperature and electricity consumption in the UK. The fitted model can then be used to predict future demand based on easily obtained temperature forecast information.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016, The Authors. This paper is licenced under a Creative Commons Attribution 3.0 Unported licence [https://creativecommons.org/licenses/by/3.0]. |
Keywords: | Bayesian estimation; big data; dynamic models; electricity consumption; hierarchical modelling; maximum likelihood; regression; supply and demand; temporal models |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Aug 2016 12:13 |
Last Modified: | 14 Aug 2018 07:56 |
Published Version: | http://www.mukpublications.com/vol-20-1-2016.php |
Status: | Published |
Publisher: | MUK Publications |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103452 |