2006
DOI: 10.1007/11941439_66
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Predicting Stock Market Time Series Using Evolutionary Artificial Neural Networks with Hurst Exponent Input Windows

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Cited by 7 publications
(10 citation statements)
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“…Given the analysis discussed above, we found six types of combinations which we can sort all listed publications ( Section 3 ) into (A table summing up these findings can be found in Appendix B ): Complexity measures as an additional criterion for analysis: [ 7 , 16 , 17 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] Complexity measures to improve the architecture of the employed neural network/ algorithm: [ 21 , 23 ] Complexity measures as additional features for machine learning algorithms: [ 16 , 20 , 30 , 31 , 32 ] Complexity measures to find regions of increased predictability: [ 22 , 24 , 28 , 29 ] Feature Selection using Complexity Measures: [ 26 , 27 , 29 ] Filtering predictions/ensembles using Complexity Measures: [ 33 ] …”
Section: Discussionmentioning
confidence: 99%
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“…Given the analysis discussed above, we found six types of combinations which we can sort all listed publications ( Section 3 ) into (A table summing up these findings can be found in Appendix B ): Complexity measures as an additional criterion for analysis: [ 7 , 16 , 17 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] Complexity measures to improve the architecture of the employed neural network/ algorithm: [ 21 , 23 ] Complexity measures as additional features for machine learning algorithms: [ 16 , 20 , 30 , 31 , 32 ] Complexity measures to find regions of increased predictability: [ 22 , 24 , 28 , 29 ] Feature Selection using Complexity Measures: [ 26 , 27 , 29 ] Filtering predictions/ensembles using Complexity Measures: [ 33 ] …”
Section: Discussionmentioning
confidence: 99%
“…In [ 32 ], a MLP among other approaches was used together with Effective Transfer Entropy (ETE, see Section 4.4 ) to predict the direction of US stocks. In [ 23 ], an MLP is used together with the Hurst exponent to predict stock market data.…”
Section: Machine Learning Methodsmentioning
confidence: 99%
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