Nonlinear Modeling for Time Series Based on the Genetic Programming and its Applications

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Jian-Jun Lu and Yun-Ling Liu and Shozo Tokinaga",
  title =        "Nonlinear Modeling for Time Series Based on the
                 Genetic Programming and its Applications",
  booktitle =    "International Conference on Machine Learning and
  year =         "2006",
  pages =        "2097--2102",
  address =      "Dalian",
  month =        aug,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0061-9",
  DOI =          "doi:10.1109/ICMLC.2006.258350",
  abstract =     "This paper deals with clustering of segments of stock
                 prices by using nonlinear modelling system for time
                 series based on the Genetic Programming (GP). We apply
                 the GP procedure in learning phase of the system where
                 we improve the nonlinear functional forms to
                 approximate the models used to generate time series.
                 The variation of the individuals with relatively high
                 capability in the pool can cope with clustering for
                 various kinds of time series which belong to the same
                 cluster similar to the classifier systems. As an
                 application, we show clustering of artificially
                 generated time series obtained by expanding or
                 shrinking by transformation functions. Then, we apply
                 the system to clustering of 8 kinds of segments of real
                 stock prices.",
  notes =        "Graduate School of Economics, Kyushu University,
                 Fukuoka 812-8581, Japan",

Genetic Programming entries for Jian-Jun Lu Yun-Ling Liu Shozo Tokinaga