The Dynamic Evolutionary Modeling of HODEs for Time Series Prediction

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  author =       "Hongqing Cao and Lishan Kang and Yuping Chen and 
                 Tao Guo",
  title =        "The Dynamic Evolutionary Modeling of HODEs for Time
                 Series Prediction",
  journal =      "Computers \& Mathematics with Applications",
  year =         "2003",
  volume =       "46",
  number =       "8-9",
  pages =        "1397--1411",
  keywords =     "genetic algorithms, genetic programming, Time series,
                 Differential equation",
  URL =          "",
  DOI =          "doi:10.1016/S0898-1221(03)90228-8",
  abstract =     "The prediction of future values of a time series
                 generated by a chaotic dynamic system is an extremely
                 challenging task. Besides some methods used in
                 traditional time series analysis, a number of nonlinear
                 prediction methods have been developed for time series
                 prediction, especially the evolutionary algorithms.
                 Many researchers have built various models by using
                 different evolutionary techniques. Different from those
                 available models, this paper presents a new idea for
                 modelling time series using higher-order ordinary
                 differential equations (HODEs) models. Accordingly, a
                 dynamic hybrid evolutionary modeling algorithm called
                 DHEMA is proposed to approach this task. Its main idea
                 is to embed a genetic algorithm (GA) into genetic
                 programming (GP) where GP is employed to optimise the
                 structure of a model, while a GA is employed to
                 optimize its parameters. By running the DHEMA, the
                 modeling and predicting processes can be carried on
                 successively and dynamically with the renewing of
                 observed data. Two practical examples are used to
                 examine the effectiveness of the algorithm in
                 performing the prediction task of time series whose
                 experimental results are compared with those of
                 standard GP.",

Genetic Programming entries for Hong-Qing Cao Li-Shan Kang Yu-Ping Chen Tao Guo