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Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Fatima, K and Lunney, TF (2005) Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets. In: IEEE SMC UK-RI Chapter Conference onApplied Cybernetics, University of London. UNSPECIFIED. 6 pp. [Conference contribution]

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Memory patterns do exist in timeseries data. Long-term or short-term predictionis possible by analysing memory patterns. The Hurst coefficient (H) is a statistical measure for predictability of time series. In this paper, memory patterns of financial data are analysedusing Hurst statistics. Experiments with radialbasis function (RBF) networks and multilayerperceptron (MLP) networks show that predictions in series with large H values aremore accurate than those with H close to 0.5.

Item Type:Conference contribution (Paper)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
ID Code:8344
Deposited By:Dr Tom Lunney
Deposited On:19 Feb 2010 13:36
Last Modified:19 Feb 2010 13:36

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