The Dynamic Evolutionary Modeling of HODEs for Time Series Prediction

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@Article{cao:2003:CMA,

• 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 = "http://www.sciencedirect.com/science/article/B6TYJ-4BRR761-P/2/4d226ed6e682798de2e1d83d01cebd95",
• 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

Citations