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.
Original language | English |
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Title of host publication | Unknown Host Publication |
Place of Publication | London |
Pages | 16-21 |
Number of pages | 6 |
Publication status | Published (in print/issue) - Sept 2005 |
Event | IEEE SMC UK-RI Chapter Conference onApplied Cybernetics - University of London Duration: 1 Sept 2005 → … |
Conference
Conference | IEEE SMC UK-RI Chapter Conference onApplied Cybernetics |
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Period | 1/09/05 → … |