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Novel stock market prediction using a hybrid model of adptive linear combiner and differential evolution

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dc.contributor.author Rout M. en_US
dc.contributor.author Majhi B. en_US
dc.contributor.author Majhi R. en_US
dc.contributor.author Panda G. en_US
dc.date.accessioned 2020-01-13T05:21:12Z
dc.date.available 2020-01-13T05:21:12Z
dc.date.issued 2011
dc.identifier.citation 4 en_US
dc.identifier.uri http://dx.doi.org/10.1007/978-3-642-19542-6_30
dc.identifier.uri http://10.10.32.48:8080/jspui/handle/2008/138
dc.description.abstract The paper proposes a novel forecasting model for efficient prediction of small and long range predictions of stock indices particularly the DJIA and S&P500. The model employs an adaptive structure containing a linear combiner with adjustable weights implemented using differential evolution. The learning algorithm using DE is dealt in details. The key features of known stock time series are extracted and used as inputs to the model for training its parameters. Exhaustive simulation study indicates that the performance of the proposed model with test input is quite satisfactory and superior to those provided by previously reported GA and PSO based forecasting models. � 2011 Springer-Verlag Berlin Heidelberg. en_US
dc.language.iso en en_US
dc.subject adaptive linear combiner en_US
dc.subject differential evolution en_US
dc.subject hybrid model en_US
dc.subject Stock market prediction en_US
dc.title Novel stock market prediction using a hybrid model of adptive linear combiner and differential evolution en_US
dc.type Conference Paper en_US


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