Time Series Modeling and Prediction Using Postfix Genetic Programming
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gp-bibliography.bib Revision:1.8081
- @InProceedings{Dabhi:2014:ACCT,
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author = "Vipul K. Dabhi and Sanjay Chaudhary",
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booktitle = "Fourth International Conference on Advanced Computing
Communication Technologies (ACCT 2014)",
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title = "Time Series Modeling and Prediction Using Postfix
Genetic Programming",
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year = "2014",
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month = feb,
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pages = "307--314",
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keywords = "genetic algorithms, genetic programming, series
Modelling, Postfix Genetic Programming, One-step ahead
prediction, Multi-step ahead prediction",
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DOI = "doi:10.1109/ACCT.2014.33",
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size = "8 pages",
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abstract = "Traditional techniques for time series modelling can
capture linear behaviour of data and lack the ability
to identify nonlinear patterns in time series.
Therefore, machine learning techniques like Neural
Network or Genetic Programming (GP) are used by
practitioners for modelling nonlinear and irregular
time series. GP is preferred over other techniques
because it does not presume model structure a priori.
This paper introduces the use of Postfix-GP, a postfix
notation based GP, for real-world nonlinear time series
modelling problems. The Postfix-GP uses linear genome
representation and stack based evaluation to reduce
space-time complexity of GP. The Postfix-GP is applied
on two real time series modelling problems: sunspots
and river flow series. Performance of evolved
Postfix-GP models on training data and out-of-sample
data are compared with those obtained by others using
EGIPSYS. The obtained results indicate that Postfix-GP
offers a new possibility for solving time series
modelling and prediction problems.",
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notes = "Also known as \cite{6783469}",
- }
Genetic Programming entries for
Vipul K Dabhi
Sanjay Chaudhary
Citations