On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data

The use of a BDF method as a tool to correct the direction of predictions made using curve fitting techniques is investigated. Random data is generated in such a fashion that it has the same properties as the data we are modelling. The data is assumed to have “memory” such that certain information i...

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Main Authors:	E. Momoniat, C. Harley, M. Berman
Format:	Article
Language:	English
Published:	Hindawi Limited 2013-01-01
Series:	Mathematical Problems in Engineering
Online Access:	http://dx.doi.org/10.1155/2013/652653

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spelling	doaj-308fd6b1ee6349319865cb2e7f9920d92020-11-24T22:37:31ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/652653652653On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related DataE. Momoniat0C. Harley1M. Berman2Centre for Differential Equations, Continuum Mechanics and Applications, School of Computational and Applied Mathematics, University of the Witwatersrand, Private Bag 3, Wits 2050, Johannesburg, South AfricaCentre for Differential Equations, Continuum Mechanics and Applications, School of Computational and Applied Mathematics, University of the Witwatersrand, Private Bag 3, Wits 2050, Johannesburg, South AfricaRAPA Pty Ltd., Level 10, 2 Bligh Street, Sydney, NSW 2000, AustraliaThe use of a BDF method as a tool to correct the direction of predictions made using curve fitting techniques is investigated. Random data is generated in such a fashion that it has the same properties as the data we are modelling. The data is assumed to have “memory” such that certain information imbedded in the data will remain within a certain range of points. Data within this period where “memory” exists—say at time steps t1,t2,…,tn—is curve-fitted to produce a prediction at the next discrete time step, tn+1. In this manner a vector of predictions is generated and converted into a discrete ordinary differential representing the gradient of the data. The BDF method implemented with this lower order approximation is used as a means of improving upon the direction of the generated predictions. The use of the BDF method in this manner improves the prediction of the direction of the time series by approximately 30%.http://dx.doi.org/10.1155/2013/652653
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	E. Momoniat C. Harley M. Berman
spellingShingle	E. Momoniat C. Harley M. Berman On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data Mathematical Problems in Engineering
author_facet	E. Momoniat C. Harley M. Berman
author_sort	E. Momoniat
title	On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data
title_short	On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data
title_full	On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data
title_fullStr	On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data
title_full_unstemmed	On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data
title_sort	on the use of backward difference formulae to improve the prediction of direction in market related data
publisher	Hindawi Limited
series	Mathematical Problems in Engineering
issn	1024-123X 1563-5147
publishDate	2013-01-01
description	The use of a BDF method as a tool to correct the direction of predictions made using curve fitting techniques is investigated. Random data is generated in such a fashion that it has the same properties as the data we are modelling. The data is assumed to have “memory” such that certain information imbedded in the data will remain within a certain range of points. Data within this period where “memory” exists—say at time steps t1,t2,…,tn—is curve-fitted to produce a prediction at the next discrete time step, tn+1. In this manner a vector of predictions is generated and converted into a discrete ordinary differential representing the gradient of the data. The BDF method implemented with this lower order approximation is used as a means of improving upon the direction of the generated predictions. The use of the BDF method in this manner improves the prediction of the direction of the time series by approximately 30%.
url	http://dx.doi.org/10.1155/2013/652653
work_keys_str_mv	AT emomoniat ontheuseofbackwarddifferenceformulaetoimprovethepredictionofdirectioninmarketrelateddata AT charley ontheuseofbackwarddifferenceformulaetoimprovethepredictionofdirectioninmarketrelateddata AT mberman ontheuseofbackwarddifferenceformulaetoimprovethepredictionofdirectioninmarketrelateddata
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On the Use of Backward Difference Formulae to Improve the Prediction of Direction in Market Related Data

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