Empirical Analysis of Model Selection Criteria for Genetic Programming in Modeling of Time Series System
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- @InProceedings{Garg:2013:CIFEr,
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author = "A. Garg and S. Sriram and K. Tai",
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title = "Empirical Analysis of Model Selection Criteria for
Genetic Programming in Modeling of Time Series System",
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booktitle = "2013 IEEE Symposium Series on Computational
Intelligence",
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year = "2013",
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editor = "P. N. Suganthan",
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pages = "90--94",
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address = "Singapore",
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month = "16-19 " # apr,
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keywords = "genetic algorithms, genetic programming, AIC, FPE,
PRESS, fitness function, model selection, stock
market",
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DOI = "doi:10.1109/CIFEr.2013.6611702",
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size = "5 pages",
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abstract = "Genetic programming (GP) and its variants have been
extensively applied for modelling of the stock markets.
To improve the generalisation ability of the model, GP
have been hybridised with its own variants (gene
expression programming (GEP), multi expression
programming (MEP)) or with the other methods such as
neural networks and boosting. The generalisation
ability of the GP model can also be improved by an
appropriate choice of model selection criterion. In the
past, several model selection criteria have been
applied. In addition, data transformations have
significant impact on the performance of the GP models.
The literature reveals that few researchers have paid
attention to model selection criterion and data
transformation while modelling stock markets using GP.
The objective of this paper is to identify the most
appropriate model selection criterion and
transformation that gives better generalised GP models.
Therefore, the present work will conduct an empirical
analysis to study the effect of three model selection
criteria across two data transformations on the
performance of GP while modelling the stock indexed in
the New York Stock Exchange (NYSE). It was found that
FPE criteria have shown a better fit for the GP model
on both data transformations as compared to other model
selection criteria.",
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notes = "CIFEr 2013, also known as \cite{6611702}",
- }
Genetic Programming entries for
Akhil Garg
Sriram Srivatsav
Kang Tai
Citations