Time Series Prediction Based on Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Jie Zuo and Changjie Tang and Chuan Li and 
                 Chang-an Yuan and An-long Chen",
  title =        "Time Series Prediction Based on Gene Expression
  booktitle =    "Advances in Web-Age Information Management: 5th
                 International Conference, WAIM 2004",
  year =         "2004",
  pages =        "55--64",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  editor =       "Qing Li and Guoren Wang and Ling Feng",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3129",
  ISBN =         "3-540-22418-1",
  address =      "Dalian, China",
  month =        "15-17 " # jul,
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Time Series Data Processing",
  DOI =          "doi:10.1007/978-3-540-27772-9_7",
  abstract =     "Two novel methods for Time Series Prediction based on
                 GEP (Gene Expression Programming). The main
                 contributions include: (1) GEP-Sliding Window
                 Prediction Method (GEP-SWPM) to mine the relationship
                 between future and historical data directly. (2)
                 GEP-Differential Equation Prediction Method (GEP-DEPM)
                 to mine ordinary differential equations from training
                 data, and predict future trends based on specified
                 initial conditions. (3) A brand new equation mining
                 method, called Differential by Microscope Interpolation
                 (DMI) that boosts the efficiency of our methods. (4) A
                 new, simple and effective GEP-constants generation
                 method called Meta-Constants (MC) is proposed. (5) It
                 is proved that a minimum expression discovered by
                 GEP-MC method with error not exceeding delta/2 uses at
                 most log3(2L/delta) operators and the problem to find
                 delta-accurate expression with fewer operators is
                 NP-hard. Extensive experiments on real data sets for
                 sun spot prediction show that the performance of the
                 new method is 20-900 times higher than existing
  notes =        "Computer Science Department, Sichuan University,
                 Chengdu, Sichuan, China, 610065",

Genetic Programming entries for Jie Zuo Changjie Tang Chuan Li Chang-an Yuan An-long Chen