Improved forecasting of time series data of real system using genetic programming
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- @InProceedings{Ahalpara:2010:gecco,
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author = "Dilip P. Ahalpara",
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title = "Improved forecasting of time series data of real
system using genetic programming",
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booktitle = "GECCO '10: Proceedings of the 12th annual conference
on Genetic and evolutionary computation",
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year = "2010",
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editor = "Juergen Branke and Martin Pelikan and Enrique Alba and
Dirk V. Arnold and Josh Bongard and
Anthony Brabazon and Juergen Branke and Martin V. Butz and
Jeff Clune and Myra Cohen and Kalyanmoy Deb and
Andries P Engelbrecht and Natalio Krasnogor and
Julian F. Miller and Michael O'Neill and Kumara Sastry and
Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and
Carsten Witt",
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isbn13 = "978-1-4503-0072-8",
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pages = "977--978",
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keywords = "genetic algorithms, genetic programming, Poster",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Portland, Oregon, USA",
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DOI = "doi:10.1145/1830483.1830658",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "A study is made to improve short term forecasting of
time series data of real system using Genetic
Programming (GP) under the framework of time delayed
embedding technique. GP based approach is used to make
analytical model of time series data of real system
using embedded vectors that help reconstruct the phase
space. The map equations, involving non-linear symbolic
expressions in the form of binary trees comprising of
time delayed components in the immediate past, are
first obtained by carrying out single-step GP fit for
the training data set and usually they are found to
give good fitness as well as single-step predictions.
However while forecasting the time series based on
multi-step predictions in the out-of-sample region in
an iterative manner, these solutions often show rapid
deterioration as we dynamically forward the solution in
future time. With a view to improve on this limitation,
it is shown that if the multi-step aspect is
incorporated while making the GP fit itself, the
corresponding GP solutions give multi-step predictions
that are improved to a fairly good extent for around
those many multi-steps as incorporated during the
multi-step GP fit. Two different methods for multi-step
fit are introduced, and the corresponding prediction
results are presented. The modified method is shown to
make better forecast for out-of-sample multi-step
predictions for the time series of a real system,
namely Electroencephelograph (EEG) signals.",
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notes = "Also known as \cite{1830658} GECCO-2010 A joint
meeting of the nineteenth international conference on
genetic algorithms (ICGA-2010) and the fifteenth annual
genetic programming conference (GP-2010)",
- }
Genetic Programming entries for
Dilip P Ahalpara
Citations