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|
|Deposited By:||Dr Tom Lunney|
|Deposited On:||19 Feb 2010 13:36|
|Last Modified:||09 Dec 2015 10:39|
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