Time series prediction model building with BP-like parameter optimization
Time series prediction model building with BP-like parameter optimization
复制标题
使用类 BP 参数优化构建时间序列预测模型
DOI：
10.1109/cec.1999.781939
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
K. Abe
中科院分区：
文献类型：
--
作者：
I. Yoshihara;M. Numata;Kenji Sugawara;S. Yamada;K. Abe
提出了一种利用遗传规划建立时间序列预测模型的方法。预测模型的构建包括两个阶段。第一个阶段是用遗传程序设计组成最合适的函数形式。第二阶段用类反向传播算法确定复合函数中的最优参数。第二阶段是局部搜索阶段，它是加速遗传算法和GP进化速度的有力工具。将该方法应用于典型的时间序列和一些真实的预测问题。计算机生成的混沌时间序列的结果进行了比较，神经网络和自回归预测。这些实验的结果证明了所提出的方法的优越性。
A method for building time series prediction model using genetic programming is proposed. The construction of prediction models consists of two stages. The first stage composes the most appropriate functional form with genetic programming. The second stage fixes optimal parameters involved in the composite function with a backpropagation-like algorithm. The second stage can be recognized as a local search, which is a powerful tool to accelerate the evolving speed of GP and GA. The method is applied to typical time series and some real world prediction problems. Results of computer generated chaotic time series were compared to those of neural network based and autoregressive predictions. The superiority of the proposed method is demonstrated in the results of these experiments.