| 复合模型在混沌时间序列中的应用 |
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| 引用本文： | 刘翔,樊重俊.复合模型在混沌时间序列中的应用[J].科技与管理,2012,14(1):23-26. |
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| 作者姓名： | 刘翔 樊重俊 |
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| 作者单位： | 上海理工大学管理学院,上海,200093 |
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| 摘 要： | 神经网络和传统线性模型结合为处理混沌时间序列提供了新的途径。将Elman神经网络和单整自回归移动平均模型结合起来,同时分析我国进出口贸易量时间序列中的线性和非线性两部分,得到更准确的预测精度。实证表明,复合模型吸收两类方法的优点,较单一模型能够更有效地预测我国进出口数据。
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| 关 键 词： | Elman回归神经网络 时间序列预测 混沌 季节性单整自回归移动平均 复合模型 |
Forecasting chaotic time series with a hybrid model |
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| LIU Xiang,FAN Chong-jun.Forecasting chaotic time series with a hybrid model[J].Science-Technology and Management,2012,14(1):23-26. |
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| Authors: | LIU Xiang FAN Chong-jun |
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| Institution: | (Business School,University of Shanghai for Science and Technology,Shanghai 200093,China) |
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| Abstract: | The combination of neural network and the traditional linear model provides a new way to deal with chaotic time series.This paper proposes a hybrid autoregressive integrated moving average(ARIMA) and Elman’s recurrent neural networks(ERNN) model to forecast international trade time series.The results show that the proposed model has more forecasting accuracy than that of single model. |
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| Keywords: | Elman＇s recurrent neural networks time series forecasting chaos seasonal autoregressive integrated moving average hybrid model |
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