Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets

K Fatima, TF Lunney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Place of PublicationLondon
Pages16-21
Number of pages6
Publication statusPublished - Sep 2005
EventIEEE SMC UK-RI Chapter Conference onApplied Cybernetics - University of London
Duration: 1 Sep 2005 → …

Conference

ConferenceIEEE SMC UK-RI Chapter Conference onApplied Cybernetics
Period1/09/05 → …

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Data storage equipment
Computer simulation
Time series
Statistics
Financial markets
Experiments

Cite this

Fatima, K., & Lunney, TF. (2005). Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets. In Unknown Host Publication (pp. 16-21). London.
Fatima, K ; Lunney, TF. / Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets. Unknown Host Publication. London, 2005. pp. 16-21
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abstract = "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.",
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Fatima, K & Lunney, TF 2005, Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets. in Unknown Host Publication. London, pp. 16-21, IEEE SMC UK-RI Chapter Conference onApplied Cybernetics, 1/09/05.

Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets. / Fatima, K; Lunney, TF.

Unknown Host Publication. London, 2005. p. 16-21.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AU - Lunney, TF

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AB - 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.

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Fatima K, Lunney TF. Memory Pattern Analysis in Time Critical Decision Modelling of Financial Markets. In Unknown Host Publication. London. 2005. p. 16-21