Learning Time Series Patterns by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{conf/acsc/XieSC12,
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author = "Feng Xie and Andy Song and Victor Ciesielski",
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title = "Learning Time Series Patterns by Genetic Programming",
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booktitle = "Thirty-Fifth Australasian Computer Science Conference,
ACSC 2012",
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year = "2012",
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editor = "Mark Reynolds and Bruce H. Thomas",
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volume = "122",
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series = "CRPIT",
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pages = "57--62",
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address = "Melbourne, Australia",
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month = jan,
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publisher = "Australian Computer Society",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-921770-03-6",
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URL = "http://crpit.com/Vol122.html",
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URL = "http://crpit.com/confpapers/CRPITV122Xie.pdf",
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bibdate = "2013-04-21",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/acsc/acsc2012.html#XieSC12",
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size = "6 pages",
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abstract = "Finding patterns such as increasing or decreasing
trends, abrupt changes and periodically repeating
sequences is a necessary task in many real world
situations. We have shown how genetic programming can
be used to detect increasingly complex patterns in time
series data. Most classification methods require a
hand-crafted feature extraction preprocessing step to
accurately perform such tasks. In contrast, the evolved
programs operate on the raw time series data. On the
more difficult problems the evolved classifiers
outperform the OneR, J48, Naive Bayes, IB1 and Adaboost
classifiers by a large margin. Furthermore this method
can handle noisy data. Our results suggest that the
genetic programming approach could be used for
detecting a wide range of patterns in time series data
without extra processing or feature extraction.",
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notes = "ACSC",
- }
Genetic Programming entries for
Feng Xie
Andy Song
Victor Ciesielski
Citations