Genetic Programming with Wavelet-Based Indicators for Financial Forecasting
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- @Article{Jin_GP_Wavelet,
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author = "Jin Li and Zhu Shi and Xiaoli Li",
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title = "Genetic Programming with Wavelet-Based Indicators for
Financial Forecasting",
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journal = "Transactions of the Institute of Measurement and
Control",
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year = "2006",
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volume = "28",
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number = "3",
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pages = "285--297",
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month = aug,
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keywords = "genetic algorithms, genetic programming, wavelet
analysis, financial forecasting",
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URL = "http://www.cs.bham.ac.uk/~jxl/cercialink/web/publication/Jin_GP_Wavelet.pdf",
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URL = "http://tim.sagepub.com/content/vol28/issue3/",
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DOI = "doi:10.1191/0142331206tim177oa",
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size = "13 pages",
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abstract = "Wavelet analysis, as a promising technique, has been
used to approach numerous problems in science and
engineering. Recent years have witnessed its novel
application in economic and finance. This paper is to
investigate whether features (or indicators) extracted
using the wavelet analysis technique could improve
financial forecasting by means of Financial Genetic
Programming (FGP), a genetic programming based
forecasting tool (i.e., Li, 2001). More specifically,
to predict whether Down Jones Industrial Average (DJIA)
Index will rise by 2.2 percent or more within the next
21 trading days, we first extract some indicators based
on wavelet coefficients of the DJIA time series using a
discrete wavelet transform; we then feed FGP with those
wavelet-based indicators to generate decision trees and
make predictions. By comparison with the prediction
performance of our previous study (i.e., Li and Tsang,
2000), it is suggested that wavelet analysis be capable
of bringing in promising indicators, and improving the
forecasting performance of FGP.",
- }
Genetic Programming entries for
Jin Li
Zhu Shi
Xiaoli Li
Citations