Forecasting time series with genetic fuzzy predictor ensemble
Forecasting time series with genetic fuzzy predictor ensemble
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DOI：
10.1109/91.649903
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发表时间：
1997-11-01
影响因子：
11.9
通讯作者：
Kim, C
中科院分区：
文献类型：
--
作者：
Kim, DJ;Kim, C
本文提出了一种遗传模糊预测集合（GFPE），用于混沌或非平稳时间序列的未来精确预测。GFPE中的每个模糊预测器都是从两个设计阶段构建的，每个阶段都由不同的遗传算法（GA）执行。第一阶段生成一个模糊规则库，它涵盖了尽可能多的训练示例。第二阶段构建微调的隶属函数，使预测误差尽可能小。这两个设计阶段在输入输出变量的不同分区组合上独立重复。通过采用等预测误差加权法调用由多个模糊预测因子组合而成的GFPE，进一步减小了预测误差。给出了在混沌时间序列和非平稳外汇汇率预测问题中的应用。在均方根误差（RMSE）方面，将该方法的预测精度与其他模糊预测器和神经网络预测器进行了比较。
This paper proposes a genetic fuzzy predictor ensemble (GFPE) for the accurate prediction of the future in the chaotic or nonstationary time series. Each fuzzy predictor in the GFPE is built from two design stages, where each stage is performed by different genetic algorithms (GA's). The first stage generates a fuzzy rule base that covers as many of training examples as possible. The second stage builds fine-tuned membership functions that make the prediction error as small as possible. These two design stages are repeated independently upon the different partition combinations of input-output variables. The prediction error will be reduced further by invoking the GFPE that combines multiple fuzzy predictors by an equal prediction error weighting method. Applications to both the Mackey-Glass chaotic time Series and the nonstationary foreign currency exchange rate prediction problem are presented. The prediction accuracy of the proposed method is compared with that of other fuzzy and neural network predictors in terms of the root mean squared error (RMSE).