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The Combined Forecasting Model Based on Wavelet Analysis in the Application of the Civil Aviation Passenger Traffic

机译：基于小波分析的基于小波分析的组合预测模型

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China's civil aviation passenger traffic was taken as the research object, the yearly passenger traffic with the theory of wavelet analysis is based on the multi-scale analysis on the time sequence to get the sequences of coefficient on different scales in order to reduce the randomness of the original sequence. And then the time sequence forecast respectively after the re-factored was based on the Grey forecasting model and exponential smoothing model. After the series of passenger traffic time series characteristics and sequence were analysis, the combination model on the wavelet analysis of non-stationary time series of Grey prediction model and exponential smoothing model was set. Finally the model for passenger traffic was applied to an empirical analysis of China's civil aviation passenger traffic. The results show that the model has higher prediction precision and small relative error, so it provides a new method to handle the original non-stationary time series prediction.
机译：中国的民航客运是研究对象，随着小波分析理论的年度客流基于对时间序列的多尺度分析，以便在不同尺度上获得系数序列以减少随机性原始序列。然后分别在重新成因后的时间顺序预测基于灰色预测模型和指数平滑模型。在乘客交通时间序列特征和序列的系列分析后，设定了非静止时间序列和指数平滑模型的小波分析的组合模型。最后，乘客交通模型应用于中国民用航空客运的实证分析。结果表明，该模型具有更高的预测精度和相对误差小，因此提供了一种处理原始非静止时间序列预测的新方法。

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